Deep Learning Based Underground Sewer Defect Classification Using a Modified RegNet

The sewer system plays an important role in protecting rainfall and treating urban wastewater.Due to the harsh internal environment and complex structure of the sewer,it is difficult to monitor the sewer system.Researchers are developing different methods,such as the Internet of Things and Artificial Intelligence,to monitor and detect the faults in the sewer system.Deep learning is a promising artificial intelligence technology that can effectively identify and classify different sewer system defects.However,the existing deep learning based solution does not provide high accuracy prediction and the defect class considered for classification is very small,which can affect the robustness of the model in the constraint environment.As a result,this paper proposes a sewer condition monitoring framework based on deep learning,which can effectively detect and evaluate defects in sewer pipelines with high accuracy.We also introduce a large dataset of sewer defects with 20 different defect classes found in the sewer pipeline.This study modified the original RegNet model by modifying the squeeze excitation(SE)block and adding the dropout layer and Leaky Rectified Linear Units(LeakyReLU)activation function in the Block structure of RegNet model.This study explored different deep learning methods such as RegNet,ResNet50,very deep convolutional networks(VGG),and GoogleNet to train on the sewer defect dataset.The experimental results indicate that the proposed system framework based on the modified-RegNet(RegNet+)model achieves the highest accuracy of 99.5 compared with the commonly used deep learning models.The proposed model provides a robust deep learning model that can effectively classify 20 different sewer defects and be utilized in real-world sewer condition monitoring applications.

Algorithmic Scheme for Concurrent Detection and Classification of Printed Circuit Board Defects

An ideal printed circuit board(PCB)defect inspection system can detect defects and classify PCB defect types.Existing defect inspection technologies can identify defects but fail to classify all PCB defect types.This research thus proposes an algorithmic scheme that can detect and categorize all 14-known PCB defect types.In the proposed algorithmic scheme,fuzzy cmeans clustering is used for image segmentation via image subtraction prior to defect detection.Arithmetic and logic operations,the circle hough transform(CHT),morphological reconstruction(MR),and connected component labeling(CCL)are used in defect classification.The algorithmic scheme achieves 100%defect detection and 99.05%defect classification accuracies.The novelty of this research lies in the concurrent use of CHT,MR,and CCL algorithms to accurately detect and classify all 14-known PCB defect types and determine the defect characteristics such as the location,area,and nature of defects.This information is helpful in electronic parts manufacturing for finding the root causes of PCB defects and appropriately adjusting the manufacturing process.Moreover,the algorithmic scheme can be integrated into machine vision to streamline the manufacturing process,improve the PCB quality,and lower the production cost.

Application of improved back-propagation algorithms in classification and detection of scars defects on rails surfaces

An experimental platform with bracket structures,cables,parallel computer and imaging system is designed for defects detecting on steel rails. Meanwhile,an improved gradient descent algorithm based on a self-adaptive learning rate and a fixed momentum factor is developed to train back-propagation neural network for accurate and efficient defects classifications. Detection results of rolling scar defects show that such detection system can achieve accurate positioning to defects edges for its improved noise suppression. More precise characteristic parameters of defects can also be extracted.Furthermore,defects classification is adopted to remedy the limitations of low convergence rate and local minimum. It can also attain the optimal training precision of 0. 00926 with the least 96 iterations. Finally,an enhanced identification rate of 95% has been confirmed for defects by using the detection system. It will also be positive in producing high-quality steel rails and guaranteeing the national transport safety.

Deep Learning-Based Algorithm for Multi-Type Defects Detection in Solar Cells with Aerial EL Images for Photovoltaic Plants

Defects detection with Electroluminescence(EL)image for photovoltaic(PV)module has become a standard test procedure during the process of production,installation,and operation of solar modules.There are some typical defects types,such as crack,finger interruption,that can be recognized with high accuracy.However,due to the complexity of EL images and the limitation of the dataset,it is hard to label all types of defects during the inspection process.The unknown or unlabeled create significant difficulties in the practical application of the automatic defects detection technique.To address the problem,we proposed an evolutionary algorithm combined with traditional image processing technology,deep learning,transfer learning,and deep clustering,which can recognize the unknown or unlabeled in the original dataset defects automatically along with the increasing of the dataset size.Specifically,we first propose a deep learning-based features extractor and defects classifier.Then,the unlabeled defects can be classified by the deep clustering algorithm and stored separately to update the original database without human intervention.When the number of unknown images reaches the preset values,transfer learning is introduced to train the classifier with the updated database.The fine-tuned model can detect new defects with high accuracy.Finally,numerical results confirm that the proposed solution can carry out efficient and accurate defect detection automatically using electroluminescence images.

Internal Defects Detection Method of the Railway Track Based on Generalization Features Cluster Under Ultrasonic Images

There may be several internal defects in railway track work that have different shapes and distribution rules,and these defects affect the safety of high-speed trains.Establishing reliable detection models and methods for these internal defects remains a challenging task.To address this challenge,in this study,an intelligent detection method based on a generalization feature cluster is proposed for internal defects of railway tracks.First,the defects are classified and counted according to their shape and location features.Then,generalized features of the internal defects are extracted and formulated based on the maximum difference between different types of defects and the maximum tolerance among same defects’types.Finally,the extracted generalized features are expressed by function constraints,and formulated as generalization feature clusters to classify and identify internal defects in the railway track.Furthermore,to improve the detection reliability and speed,a reduced-dimension method of the generalization feature clusters is presented in this paper.Based on this reduced-dimension feature and strongly constrained generalized features,the K-means clustering algorithm is developed for defect clustering,and good clustering results are achieved.Regarding the defects in the rail head region,the clustering accuracy is over 95%,and the Davies-Bouldin index(DBI)index is negligible,which indicates the validation of the proposed generalization features with strong constraints.Experimental results prove that the accuracy of the proposed method based on generalization feature clusters is up to 97.55%,and the average detection time is 0.12 s/frame,which indicates that it performs well in adaptability,high accuracy,and detection speed under complex working environments.The proposed algorithm can effectively detect internal defects in railway tracks using an established generalization feature cluster model.

Computer-Assisted Real-Time Rice Variety Learning Using Deep Learning Network

Due to the inconsistency of rice variety,agricultural industry faces an important challenge of rice grading and classification by the traditional grading system.The existing grading system is manual,which introduces stress and strain to humans due to visual inspection.Automated rice grading system development has been proposed as a promising research area in computer vision.In this study,an accurate deep learning-based non-contact and cost-effective rice grading system was developed by rice appearance and characteristics.The proposed system provided real-time processing by using a NI-myRIO with a high-resolution camera and user interface.We firstly trained the network by a rice public dataset to extract rice discriminative features.Secondly,by using transfer learning,the pre-trained network was used to locate the region by extracting a feature map.The proposed deep learning model was tested using two public standard datasets and a prototype real-time scanning system.Using AlexNet architecture,we obtained an average accuracy of 98.2%with 97.6%sensitivity and 96.4%specificity.To validate the real-time performance of proposed rice grading classification system,various performance indices were calculated and compared with the existing classifier.Both simulation and real-time experiment evaluations confirmed the robustness and reliability of the proposed rice grading system.

A Smart Deep Convolutional Neural Network for Real-Time Surface Inspection

A proper detection and classification of defects in steel sheets in real time have become a requirement for manufacturing these products,largely used in many industrial sectors.However,computers used in the production line of small to medium size companies,in general,lack performance to attend real-time inspection with high processing demands.In this paper,a smart deep convolutional neural network for using in real-time surface inspection of steel rolling sheets is proposed.The architecture is based on the state-of-the-art SqueezeNet approach,which was originally developed for usage with autonomous vehicles.The main features of the proposed model are:small size and low computational burden.The model is 10 to 20 times smaller when compared to other networks designed for the same task,and more than 700 times smaller than general networks.Also,the number of floating-point operations for a prediction is about 50 times lower than the ones used for similar tasks.Despite its small size,the proposed model achieved near-perfect accuracy on a public dataset of 1800 images of six types of steel rolling defects.