A Lightweight Network with Dual Encoder and Cross Feature Fusion for Cement Pavement Crack Detection

Automatic crack detection of cement pavement chiefly benefits from the rapid development of deep learning,with convolutional neural networks(CNN)playing an important role in this field.However,as the performance of crack detection in cement pavement improves,the depth and width of the network structure are significantly increased,which necessitates more computing power and storage space.This limitation hampers the practical implementation of crack detection models on various platforms,particularly portable devices like small mobile devices.To solve these problems,we propose a dual-encoder-based network architecture that focuses on extracting more comprehensive fracture feature information and combines cross-fusion modules and coordinated attention mechanisms formore efficient feature fusion.Firstly,we use small channel convolution to construct shallow feature extractionmodule(SFEM)to extract low-level feature information of cracks in cement pavement images,in order to obtainmore information about cracks in the shallowfeatures of images.In addition,we construct large kernel atrous convolution(LKAC)to enhance crack information,which incorporates coordination attention mechanism for non-crack information filtering,and large kernel atrous convolution with different cores,using different receptive fields to extract more detailed edge and context information.Finally,the three-stage feature map outputs from the shallow feature extraction module is cross-fused with the two-stage feature map outputs from the large kernel atrous convolution module,and the shallow feature and detailed edge feature are fully fused to obtain the final crack prediction map.We evaluate our method on three public crack datasets:DeepCrack,CFD,and Crack500.Experimental results on theDeepCrack dataset demonstrate the effectiveness of our proposed method compared to state-of-the-art crack detection methods,which achieves Precision(P)87.2%,Recall(R)87.7%,and F-score(F1)87.4%.Thanks to our lightweight crack detectionmodel,the parameter count of the model in real-world detection scenarios has been significantly reduced to less than 2M.This advancement also facilitates technical support for portable scene detection.

Lightweight Malicious Code Classification Method Based on Improved Squeeze Net

With the growth of the Internet,more and more business is being done online,for example,online offices,online education and so on.While this makes people’s lives more convenient,it also increases the risk of the network being attacked by malicious code.Therefore,it is important to identify malicious codes on computer systems efficiently.However,most of the existing malicious code detection methods have two problems:(1)The ability of the model to extract features is weak,resulting in poor model performance.(2)The large scale of model data leads to difficulties deploying on devices with limited resources.Therefore,this paper proposes a lightweight malicious code identification model Lightweight Malicious Code Classification Method Based on Improved SqueezeNet(LCMISNet).In this paper,the MFire lightweight feature extraction module is constructed by proposing a feature slicing module and a multi-size depthwise separable convolution module.The feature slicing module reduces the number of parameters by grouping features.The multi-size depthwise separable convolution module reduces the number of parameters and enhances the feature extraction capability by replacing the standard convolution with depthwise separable convolution with different convolution kernel sizes.In addition,this paper also proposes a feature splicing module to connect the MFire lightweight feature extraction module based on the feature reuse and constructs the lightweight model LCMISNet.The malicious code recognition accuracy of LCMISNet on the BIG 2015 dataset and the Malimg dataset reaches 98.90% and 99.58%,respectively.It proves that LCMISNet has a powerful malicious code recognition performance.In addition,compared with other network models,LCMISNet has better performance,and a lower number of parameters and computations.

A Lightweight Convolutional Neural Network with Hierarchical Multi-Scale Feature Fusion for Image Classification

Convolutional neural networks (CNNs) are widely used in image classification tasks, but their increasing model size and computation make them challenging to implement on embedded systems with constrained hardware resources. To address this issue, the MobileNetV1 network was developed, which employs depthwise convolution to reduce network complexity. MobileNetV1 employs a stride of 2 in several convolutional layers to decrease the spatial resolution of feature maps, thereby lowering computational costs. However, this stride setting can lead to a loss of spatial information, particularly affecting the detection and representation of smaller objects or finer details in images. To maintain the trade-off between complexity and model performance, a lightweight convolutional neural network with hierarchical multi-scale feature fusion based on the MobileNetV1 network is proposed. The network consists of two main subnetworks. The first subnetwork uses a depthwise dilated separable convolution (DDSC) layer to learn imaging features with fewer parameters, which results in a lightweight and computationally inexpensive network. Furthermore, depthwise dilated convolution in DDSC layer effectively expands the field of view of filters, allowing them to incorporate a larger context. The second subnetwork is a hierarchical multi-scale feature fusion (HMFF) module that uses parallel multi-resolution branches architecture to process the input feature map in order to extract the multi-scale feature information of the input image. Experimental results on the CIFAR-10, Malaria, and KvasirV1 datasets demonstrate that the proposed method is efficient, reducing the network parameters and computational cost by 65.02% and 39.78%, respectively, while maintaining the network performance compared to the MobileNetV1 baseline.

Residual Feature Attentional Fusion Network for Lightweight Chest CT Image Super-Resolution

The diagnosis of COVID-19 requires chest computed tomography(CT).High-resolution CT images can provide more diagnostic information to help doctors better diagnose the disease,so it is of clinical importance to study super-resolution(SR)algorithms applied to CT images to improve the reso-lution of CT images.However,most of the existing SR algorithms are studied based on natural images,which are not suitable for medical images;and most of these algorithms improve the reconstruction quality by increasing the network depth,which is not suitable for machines with limited resources.To alleviate these issues,we propose a residual feature attentional fusion network for lightweight chest CT image super-resolution(RFAFN).Specifically,we design a contextual feature extraction block(CFEB)that can extract CT image features more efficiently and accurately than ordinary residual blocks.In addition,we propose a feature-weighted cascading strategy(FWCS)based on attentional feature fusion blocks(AFFB)to utilize the high-frequency detail information extracted by CFEB as much as possible via selectively fusing adjacent level feature information.Finally,we suggest a global hierarchical feature fusion strategy(GHFFS),which can utilize the hierarchical features more effectively than dense concatenation by progressively aggregating the feature information at various levels.Numerous experiments show that our method performs better than most of the state-of-the-art(SOTA)methods on the COVID-19 chest CT dataset.In detail,the peak signal-to-noise ratio(PSNR)is 0.11 dB and 0.47 dB higher on CTtest1 and CTtest2 at×3 SR compared to the suboptimal method,but the number of parameters and multi-adds are reduced by 22K and 0.43G,respectively.Our method can better recover chest CT image quality with fewer computational resources and effectively assist in COVID-19.

High performance“non-local”generic face reconstruction model using the lightweight Speckle-Transformer(SpT)UNet

Significant progress has been made in computational imaging(CI),in which deep convolutional neural networks(CNNs)have demonstrated that sparse speckle patterns can be reconstructed.However,due to the limited“local”kernel size of the convolutional operator,for the spatially dense patterns,such as the generic face images,the performance of CNNs is limited.Here,we propose a“non-local”model,termed the Speckle-Transformer(SpT)UNet,for speckle feature extraction of generic face images.It is worth noting that the lightweight SpT UNet reveals a high efficiency and strong comparative performance with Pearson Correlation Coefficient(PCC),and structural similarity measure(SSIM)exceeding 0.989,and 0.950,respectively.

Configuration Synthesis and Lightweight Networking of Deployable Mechanism Based on a Novel Pyramid Module

Deployable mechanism with preferable deployable performance,strong expansibility,and lightweight has attracted much attention because of their potential in aerospace.A basic deployable pyramid unit with good deployability and expandability is proposed to construct a sizeable deployable mechanism.Firstly,the basic unit folding principle and expansion method is proposed.The configuration synthesis method of adding constraint chains of spatial closed-loop mechanism is used to synthesize the basic unit.Then,the degree of freedom of the basic unit is analyzed using the screw theory and the link dismantling method.Next,the three-dimensional models of the pyramid unit,expansion unit,and array unit are established,and the folding motion simulation analysis is carried out.Based on the number of components,weight reduction rate,and deployable rate,the performance characteristics of the three types of mechanisms are described in detail.Finally,prototypes of the pyramid unit,combination unit,and expansion unit are developed to verify further the correctness of the configuration synthesis based on the pyramid.The proposed deployable mechanism provides aference for the design and application of antennas with a large aperture,high deployable rate,and lightweight.It has a good application prospect in the aerospace field.

Wafer map defect patterns classification based on a lightweight network and data augmentation

Accurately identifying defect patterns in wafer maps can help engineers find abnormal failure factors in production lines.During the wafer testing stage,deep learning methods are widely used in wafer defect detection due to their powerful feature extraction capa-bilities.However,most of the current wafer defect patterns classification models have high complexity and slow detection speed,which are difficult to apply in the actual wafer production process.In addition,there is a data imbalance in the wafer dataset that seriously affects the training results of the model.To reduce the complexity of the deep model without affecting the wafer feature expression,this paper adjusts the structure of the dense block in the PeleeNet network and proposes a lightweight network WM‐PeleeNet based on the PeleeNet module.In addition,to reduce the impact of data imbalance on model training,this paper proposes a wafer data augmentation method based on a convolutional autoencoder by adding random Gaussian noise to the hidden layer.The method proposed in this paper has an average accuracy of 95.4%on the WM‐811K wafer dataset with only 173.643 KB of the parameters and 316.194 M of FLOPs,and takes only 22.99 s to detect 1000 wafer pictures.Compared with the original PeleeNet network without optimization,the number of parameters and FLOPs are reduced by 92.68%and 58.85%,respectively.Data augmentation on the minority class wafer map improves the average classification accuracy by 1.8%on the WM‐811K dataset.At the same time,the recognition accuracy of minority classes such as Scratch pattern and Donut pattern are significantly improved.

A Lightweight Electronic Water Pump Shell Defect Detection Method Based on Improved YOLOv5s

For surface defects in electronic water pump shells,the manual detection efficiency is low,prone to misdetection and leak detection,and encounters problems,such as uncertainty.To improve the speed and accuracy of surface defect detection,a lightweight detection method based on an improved YOLOv5s method is proposed to replace the traditional manual detection methods.In this method,the MobileNetV3 module replaces the backbone network of YOLOv5s,depth-separable convolution is introduced,the parameters and calculations are reduced,and CIoU_Loss is used as the loss function of the boundary box regression to improve its detection accuracy.A dataset of electronic pump shell defects is established,and the performance of the improved method is evaluated by comparing it with that of the original method.The results show that the parameters and FLOPs are reduced by 49.83%and 61.59%,respectively,compared with the original YOLOv5s model,and the detection accuracy is improved by 1.74%,which is an indication of the superiority of the improved method.To further verify the universality of the improved method,it is compared with the results using the original method on the PASCALVOC2007 dataset,which verifies that it yields better performance.In summary,the improved lightweight method can be used for the real-time detection of electronic water pump shell defects.

Lightweight Multi-scale Convolutional Neural Network for Rice Leaf Disease Recognition

In the field of agricultural information,the identification and prediction of rice leaf disease have always been the focus of research,and deep learning(DL)technology is currently a hot research topic in the field of pattern recognition.The research and development of high-efficiency,highquality and low-cost automatic identification methods for rice diseases that can replace humans is an important means of dealing with the current situation from a technical perspective.This paper mainly focuses on the problem of huge parameters of the Convolutional Neural Network(CNN)model and proposes a recognitionmodel that combines amulti-scale convolution module with a neural network model based on Visual Geometry Group(VGG).The accuracy and loss of the training set and the test set are used to evaluate the performance of the model.The test accuracy of this model is 97.1%that has increased 5.87%over VGG.Furthermore,the memory requirement is 26.1M,only 1.6%of the VGG.Experiment results show that this model performs better in terms of accuracy,recognition speed and memory size.

Lightweight Classification Network for Pulmonary Tuberculosis Based on CT Images

With the continuous development of medical informatics and digital diagnosis,the classification of tuberculosis(TB)cases from computed tomography(CT)images of the lung based on deep learning is an important guiding aid in clinical diagnosis and treatment.Due to its potential application in medical image classification,this task has received extensive research attention.Existing related neural network techniques are still challenging in terms of feature extraction of global contextual information of images and network complexity in achieving image classification.To address these issues,this paper proposes a lightweight medical image classification network based on a combination of Transformer and convolutional neural network(CNN)for the classification of TB cases from lung CT.The method mainly consists of a fusion of the CNN module and the Transformer module,exploiting the advantages of both in order to accomplish a more accurate classification task.On the one hand,the CNN branch supplements the Transformer branch with basic local feature information in the low level;on the other hand,in the middle and high levels of the model,the CNN branch can also provide the Transformer architecture with different local and global feature information to the Transformer architecture to enhance the ability of the model to obtain feature information and improve the accuracy of image classification.A shortcut is used in each module of the network to solve the problem of poor model results due to gradient divergence and to optimize the effectiveness of TB classification.The proposed lightweight model can well solve the problem of long training time in the process of TB classification of lung CT and improve the speed of classification.The proposed method was validated on a CT image data set provided by the First Hospital of Lanzhou University.The experimental results show that the proposed lightweight classification network for TB based on CT medical images of lungs can fully extract the feature information of the input images and obtain high-accuracy classification results.

A network lightweighting method for difficult segmentation of 3D medical images

Currently,deep learning is widely used in medical image segmentation and has achieved good results.However,3D medical image segmentation tasks with diverse lesion characters,blurred edges,and unstable positions require complex networks with a large number of parameters.It is computationally expensive and results in high requirements on equipment,making it hard to deploy the network in hospitals.In this work,we propose a method for network lightweighting and applied it to a 3D CNN based network.We experimented on a COVID-19 lesion segmentation dataset.Specifically,we use three cascaded one-dimensional convolutions to replace a 3D convolution,and integrate instance normalization with the previous layer of one-dimensional convolutions to accelerate network inference.In addition,we simplify test-time augmentation and deep supervision of the network.Experiments show that the lightweight network can reduce the prediction time of each sample and the memory usage by 50%and reduce the number of parameters by 60%compared with the original network.The training time of one epoch is also reduced by 50%with the segmentation accuracy dropped within the acceptable range.

Research on Track Fastener Service Status Detection Based on Improved Yolov4 Model

As an important part of railway lines, the healthy service status of track fasteners was very important to ensure the safety of trains. The application of deep learning algorithms was becoming an important method to realize its state detection. However, there was often a deficiency that the detection accuracy and calculation speed of model were difficult to balance, when the traditional deep learning model is used to detect the service state of track fasteners. Targeting this issue, an improved Yolov4 model for detecting the service status of track fasteners was proposed. Firstly, the Mixup data augmentation technology was introduced into Yolov4 model to enhance the generalization ability of model. Secondly, the MobileNet-V2 lightweight network was employed in lieu of the CSPDarknet53 network as the backbone, thereby reducing the number of algorithm parameters and improving the model’s computational efficiency. Finally, the SE attention mechanism was incorporated to boost the importance of rail fastener identification by emphasizing relevant image features, ensuring that the network’s focus was primarily on the fasteners being inspected. The algorithm achieved both high precision and high speed operation of the rail fastener service state detection, while realizing the lightweight of model. The experimental results revealed that, the MAP value of the rail fastener service state detection algorithm based on the improved Yolov4 model reaches 83.2%, which is 2.83% higher than that of the traditional Yolov4 model, and the calculation speed was improved by 67.39%. Compared with the traditional Yolov4 model, the proposed method achieved the collaborative optimization of detection accuracy and calculation speed.

Low-altitude small-sized object detection using lightweight feature-enhanced convolutional neural network

Unauthorized operations referred to as“black flights”of unmanned aerial vehicles(UAVs)pose a significant danger to public safety,and existing low-attitude object detection algorithms encounter difficulties in balancing detection precision and speed.Additionally,their accuracy is insufficient,particularly for small objects in complex environments.To solve these problems,we propose a lightweight feature-enhanced convolutional neural network able to perform detection with high precision detection for low-attitude flying objects in real time to provide guidance information to suppress black-flying UAVs.The proposed network consists of three modules.A lightweight and stable feature extraction module is used to reduce the computational load and stably extract more low-level feature,an enhanced feature processing module significantly improves the feature extraction ability of the model,and an accurate detection module integrates low-level and advanced features to improve the multiscale detection accuracy in complex environments,particularly for small objects.The proposed method achieves a detection speed of 147 frames per second(FPS)and a mean average precision(mAP)of 90.97%for a dataset composed of flying objects,indicating its potential for low-altitude object detection.Furthermore,evaluation results based on microsoft common objects in context(MS COCO)indicate that the proposed method is also applicable to object detection in general.

Disease Recognition of Apple Leaf Using Lightweight Multi-Scale Network with ECANet

To solve the problem of difficulty in identifying apple diseases in the natural environment and the low application rate of deep learning recognition networks,a lightweight ResNet(LW-ResNet)model for apple disease recognition is proposed.Based on the deep residual network(ResNet18),the multi-scale feature extraction layer is constructed by group convolution to realize the compression model and improve the extraction ability of different sizes of lesion features.By improving the identity mapping structure to reduce information loss.By introducing the efficient channel attention module(ECANet)to suppress noise from a complex background.The experimental results show that the average precision,recall and F1-score of the LW-ResNet on the test set are 97.80%,97.92%and 97.85%,respectively.The parameter memory is 2.32 MB,which is 94%less than that of ResNet18.Compared with the classic lightweight networks SqueezeNet and MobileNetV2,LW-ResNet has obvious advantages in recognition performance,speed,parameter memory requirement and time complexity.The proposed model has the advantages of low computational cost,low storage cost,strong real-time performance,high identification accuracy,and strong practicability,which can meet the needs of real-time identification task of apple leaf disease on resource-constrained devices.

Face Age Estimation Based on CSLBP and Lightweight Convolutional Neural Network

As the use of facial attributes continues to expand,research into facial age estimation is also developing.Because face images are easily affected by factors including illumination and occlusion,the age estimation of faces is a challenging process.This paper proposes a face age estimation algorithm based on lightweight convolutional neural network in view of the complexity of the environment and the limitations of device computing ability.Improving face age estimation based on Soft Stagewise Regression Network(SSR-Net)and facial images,this paper employs the Center Symmetric Local Binary Pattern(CSLBP)method to obtain the feature image and then combines the face image and the feature image as network input data.Adding feature images to the convolutional neural network can improve the accuracy as well as increase the network model robustness.The experimental results on IMDB-WIKI and MORPH 2 datasets show that the lightweight convolutional neural network method proposed in this paper reduces model complexity and increases the accuracy of face age estimations.

Identification of Rice Leaf Disease Using Improved ShuffleNet V2

Accurate identification of rice diseases is crucial for controlling diseases and improving rice yield.To improve the classification accuracy of rice diseases,this paper proposed a classification and identification method based on an improved ShuffleNet V2(GE-ShuffleNet)model.Firstly,the Ghost module is used to replace the 1×1 convolution in the two basic unit modules of ShuffleNet V2,and the unimportant 1×1 convolution is deleted from the two basic unit modules of ShuffleNet V2.The Hardswish activation function is applied to replace the ReLU activation function to improve the identification accuracy of the model.Secondly,an effective channel attention(ECA)module is added to the network to avoid dimension reduction,and the correlation between channels is effectively extracted through 1D convolution.Besides,L2 regularization is introduced to fine-tune the training parameters during training to prevent overfitting.Finally,the considerable experimental and numerical results proved the advantages of our proposed model in terms of model size,floating-point operation per second(FLOPs),and parameters(Params).Especially in the case of smaller model size(5.879 M),the identification accuracy of GE-ShuffleNet(96.6%)is higher than that of ShuffleNet V2(94.4%),MobileNet V2(93.7%),AlexNet(79.1%),Swim Transformer(88.1%),EfficientNet V2(89.7%),VGG16(81.9%),GhostNet(89.3%)and ResNet50(92.5%).

Fast mode decomposition for few-mode fiber based on lightweight neural network

In this paper,we present a fast mode decomposition method for few-mode fibers,utilizing a lightweight neural network called MobileNetV3-Light.This method can quickly and accurately predict the amplitude and phase information of different modes,enabling us to fully characterize the optical field without the need for expensive experimental equipment.We train the MobileNetV3-Light using simulated near-field optical field maps,and evaluate its performance using both simulated and reconstructed near-field optical field maps.To validate the effectiveness of this method,we conduct mode decomposition experiments on a few-mode fiber supporting six linear polarization(LP)modes(LP01,LP11e,LP11o,LP21e,LP21o,LP02).The results demonstrate a remarkable average correlation of 0.9995 between our simulated and reconstructed near-field lightfield maps.And the mode decomposition speed is about 6 ms per frame,indicating its powerful real-time processing capability.In addition,the proposed network model is compact,with a size of only 6.5 MB,making it well suited for deployment on portable mobile devices.

A novel lightweight computerized ECG interpretation approach based on clinical 12-lead data

Although 12-lead electrocardiograms(ECGs)provide a wide range of spatiotemporal characteristics,interpreting them for arrhythmia detection is difficult due to a lack of reliable large-scale clinical datasets.Herein,we proposed an innovative lightweight computerized ECG interpretation approach based on 12-lead data.Our model was trained,validated,and tested on 53845 standard 12-lead ECG records collected at Shanghai First People’s Hospital in affiliation with Shanghai Jiao Tong University.The experiments revealed that our approach had a classification accuracy of 94.41%in the classification task of seven types of rhythms,which was markedly superior to related single-lead and 12-lead ECG classification methods.Moreover,the average receiver operating characteristic area under the curve reached a value of 0.940,and the precision values for sinus tachycardia and sinus bradycardia were 0.945 and 0.91,respectively,with specificity values of 0.996 and 0.994.By employing our boosting method,we were able to improve the accuracy to 94.85%.To investigate the performance degradation of the proposed neural network in some classes,an ECG cardiologist was enlisted to review questionable ECGs;this process provides a promising direction for network performance improvement.Therefore,the proposed computerized ECG interpretation approach has practical significance because it could help professional physicians analyze patients’heart conditions based on real-time 12-lead ECG or grade their disease severity in advance.

A Study on Enhancing Chip Detection Efficiency Using the Lightweight Van-YOLOv8 Network

In pursuit of cost-effective manufacturing,enterprises are increasingly adopting the practice of utilizing recycled semiconductor chips.To ensure consistent chip orientation during packaging,a circular marker on the front side is employed for pin alignment following successful functional testing.However,recycled chips often exhibit substantial surface wear,and the identification of the relatively small marker proves challenging.Moreover,the complexity of generic target detection algorithms hampers seamless deployment.Addressing these issues,this paper introduces a lightweight YOLOv8s-based network tailored for detecting markings on recycled chips,termed Van-YOLOv8.Initially,to alleviate the influence of diminutive,low-resolution markings on the precision of deep learning models,we utilize an upscaling approach for enhanced resolution.This technique relies on the Super-Resolution Generative Adversarial Network with Extended Training(SRGANext)network,facilitating the reconstruction of high-fidelity images that align with input specifications.Subsequently,we replace the original YOLOv8smodel’s backbone feature extraction network with the lightweight VanillaNetwork(VanillaNet),simplifying the branch structure to reduce network parameters.Finally,a Hybrid Attention Mechanism(HAM)is implemented to capture essential details from input images,improving feature representation while concurrently expediting model inference speed.Experimental results demonstrate that the Van-YOLOv8 network outperforms the original YOLOv8s on a recycled chip dataset in various aspects.Significantly,it demonstrates superiority in parameter count,computational intricacy,precision in identifying targets,and speed when compared to certain prevalent algorithms in the current landscape.The proposed approach proves promising for real-time detection of recycled chips in practical factory settings.

无线传感网中轻型简单网络管理协议(英文)

A Light-Weight Simple Network Management Protocol (LW-SNMP) for the wireless sensor network is proposed, which is a kind of hierarchical network management system including a sink manager, cluster proxies, and node agents. Considering the resource limitations on the sensor nodes, we design new management messages, new data types and new management information base completely. The management messages between the cluster proxy and node agents are delivered as normal data packets. The experiment results show that LW-SNMP can meet the management demands in the resource-limited wireless sensor networks and has a good performance in stability, effectiveness of memory, extensibility than the traditional Simple Network Management Protocol (SNMP).