Traffic Sign Recognition for Autonomous Vehicle Using Optimized YOLOv7 and Convolutional Block Attention Module

The infrastructure and construction of roads are crucial for the economic and social development of a region,but traffic-related challenges like accidents and congestion persist.Artificial Intelligence(AI)and Machine Learning(ML)have been used in road infrastructure and construction,particularly with the Internet of Things(IoT)devices.Object detection in Computer Vision also plays a key role in improving road infrastructure and addressing trafficrelated problems.This study aims to use You Only Look Once version 7(YOLOv7),Convolutional Block Attention Module(CBAM),the most optimized object-detection algorithm,to detect and identify traffic signs,and analyze effective combinations of adaptive optimizers like Adaptive Moment estimation(Adam),Root Mean Squared Propagation(RMSprop)and Stochastic Gradient Descent(SGD)with the YOLOv7.Using a portion of German traffic signs for training,the study investigates the feasibility of adopting smaller datasets while maintaining high accuracy.The model proposed in this study not only improves traffic safety by detecting traffic signs but also has the potential to contribute to the rapid development of autonomous vehicle systems.The study results showed an impressive accuracy of 99.7%when using a batch size of 8 and the Adam optimizer.This high level of accuracy demonstrates the effectiveness of the proposed model for the image classification task of traffic sign recognition.

ANC: Attention Network for COVID-19 Explainable Diagnosis Based on Convolutional Block Attention Module

Aim: To diagnose COVID-19 more efficiently and more correctly, this study proposed a novel attention network forCOVID-19 (ANC). Methods: Two datasets were used in this study. An 18-way data augmentation was proposed toavoid overfitting. Then, convolutional block attention module (CBAM) was integrated to our model, the structureof which is fine-tuned. Finally, Grad-CAM was used to provide an explainable diagnosis. Results: The accuracyof our ANC methods on two datasets are 96.32% ± 1.06%, and 96.00% ± 1.03%, respectively. Conclusions: Thisproposed ANC method is superior to 9 state-of-the-art approaches.

MobileNet network optimization based on convolutional block attention module

Deep learning technology is widely used in computer vision.Generally,a large amount of data is used to train the model weights in deep learning,so as to obtain a model with higher accuracy.However,massive data and complex model structures require more calculating resources.Since people generally can only carry and use mobile and portable devices in application scenarios,neural networks have limitations in terms of calculating resources,size and power consumption.Therefore,the efficient lightweight model MobileNet is used as the basic network in this study for optimization.First,the accuracy of the MobileNet model is improved by adding methods such as the convolutional block attention module(CBAM)and expansion convolution.Then,the MobileNet model is compressed by using pruning and weight quantization algorithms based on weight size.Afterwards,methods such as Python crawlers and data augmentation are employed to create a garbage classification data set.Based on the above model optimization strategy,the garbage classification mobile terminal application is deployed on mobile phones and raspberry pies,realizing completing the garbage classification task more conveniently.

Numerical Analyses of Electromagnetic Forces on the ITER Blanket Module Shield Block During Major Disruptions

Electromagnetic（EM） load is one of the key design drivers for the blanket shield block（SB） and other in-vessel components. In this article, an EM analysis method was developed to address the EM force on the SB. The plasma currents, which vary spatially and temporally,are loaded as a filament at each time point. The standard blanket module No.04（BM04） under major disruption（MD） is selected to perform the analyses. The analyses results are validated by comparing currents on the passive structure. To better understand the effects of cooling channels and slits on the EM force, the case of SB without cooling channel and the case without slits are calculated to make comparisons. The results show that the slits play an important role in controlling the EM load on SB.

基于改进YOLOv5s的轻量级绝缘子缺失检测

针对现有绝缘子缺失检测模型计算复杂度高和小目标难以检测等问题,提出一种基于改进的YOLOv5s轻量级检测模型。首先,移除主干网络中的C3模块来减少模型的参数量。其次,在多尺度特征融合网络中引入卷积块注意力机制来提高复杂背景下模型的特征提取能力。同时,采用加权双向特征金字塔网络结构对特征进行双向跨尺度加权融合,提升网络在遮挡物、相似目标干扰下目标的检测性能。最后,选用SIoU损失函数提升网络的收敛速度和检测精度。实验结果表明,所提模型的平均精准率为96.8%,浮点运算数为2.8 GFLOPS,而原始YOLOv5s在保证97.4%的平均精准率下的浮点运算数为16.3 GFLOPS。相较于原始模型,所提模型对小目标、遮挡目标以及模糊等场景有着较强的鲁棒性,且在保证近似检测精度的同时极大减少了计算量。

融合Inception V1-CBAM-CNN的轴承剩余寿命预测模型

针对现有的滚动轴承剩余寿命(RUL)预测方法精度低、轴承健康指标(HI)构建困难等问题,提出了一种基于卷积神经网络(CNN)并融合Inception V1模块和卷积注意力机制模块(CBAM)的滚动轴承RUL预测模型。首先,在CNN中添加了CBAM机制,并进行了加权处理,在通道和空间维度对重要特征进行了强化,对次要特征进行了抑制,通过添加改进的InceptionV1模块,提高了CNN通道间信息交互水平,全面提取了退化特征;然后,进行了网络优化,采用全局最大池化(GMP)方法对模型进行了简化,采用Dropout和批量归一化(BN)方法,避免了过拟合,提高了精度,且克服了训练时出现的梯度消失问题;最后,对数据进行了处理,将降噪后的信号重组为三维张量,将其作为HI,构建了退化标签,引入了评价指标,采用PHM2012轴承数据集进行了实验验证,在3种工况下将其与深度神经网络(DNN)、CNN方法、结合注意力机制的残差网络方法(ResNet)进行了对比。研究结果表明:该方法在变负载条件下的平均RMSE为0.033,较其他方法的RMSE值分别降低了86%、78%和69%,在预测精度和泛化能力方面具有明显优势。

虚拟I/O模块在DCS以太网通信接口模块上的实现

为减少硬件模块的重复开发,在保留原DCS软硬件系统架构不变的基础上,在以太网通信接口模块硬件上虚拟不同的I/O模块。虚拟I/O模块与硬点I/O模块具有一致的输入、输出及参数属性,且组态方式和产物格式一致。同一以太网通信模块上可在不同的虚拟I/O槽位上配置不同的虚拟I/O模块,如Modbus TCP虚拟I/O模块和GOOSE虚拟I/O模块,进而在同一硬件实现不同的通信功能。虚拟I/O模块的使用一方面减少了DCS系统硬件模块的种类,简化备品备件,另一方面也提高了通信扩展的灵活性和便利性。

基于CBAM-CGRU-SVM的Android恶意软件检测方法

随着Android恶意软件的种类和数量不断增多,检测恶意软件以保护系统安全和用户隐私变得越来越重要。针对传统的恶意软件检测模型分类准确率较低的问题,提出一种基于卷积神经网络(CNN)、门控循环单元(GRU)和支持向量机(SVM)的模型CBAM-CGRU-SVM。首先,在CNN中添加卷积块注意力模块(CBAM)以学习更多恶意软件的关键特征;其次,利用GRU进一步提取特征;最后,为了解决图像分类时模型泛化能力不足的问题,使用SVM代替softmax激活函数作为模型的分类函数。实验使用了Malimg公开数据集,该数据集将恶意软件数据图像化作为模型输入。实验结果表明,CBAM-CGRU-SVM模型分类准确率达到94.73%,能够更有效地对恶意软件家族进行分类。

基于YOLO算法的非机动车辆检测模型

针对自动驾驶车辆目标检测过程中非机动车因体积小、易被遮挡而导致误检和漏检的问题,为提高非机动车的检测精度,对YOLOv4基础算法进行改进,利用跨阶段连接优化特征提取融合网络,在减少计算量的同时提高检测性能,并嵌入卷积块注意力模块(CBAM),通过通道和空间注意力权值分配来增大有效特征权重、提高检测精度,同时,利用自建的非机动车数据集,在锚框(Anchor)自适应匹配的基础上建立非机动车检测模型。最后,为验证模型的有效性,通过消融实验对比模型性能,结果表明,所提出的检测模型能够有效提高非机动车的检测和识别效果,较好地解决误检和漏检问题。

改进YOLOv5的无人机影像道路目标检测算法

针对无人机影像中道路小目标漏检和目标之间遮挡导致的目标检测精度低、鲁棒性差等问题,提出一种多尺度融合卷积注意力模块(Convolutional block attention module,CBAM)的YOLOv5道路目标检测算法,即YOLOv5s-FCC。首先,引入小目标感知层对模型进行多尺度改进,增加一个针对小目标的YOLO检测头以提高网络对道路中小目标的特征提取能力。其次,利用CBAM融合空间和通道信息以增强网络中的重要信息,通过将CBAM引入Backbone主干网络不同位置,以获得CBAM最佳融合位置。最后,采用CIo U作为损失函数,以提高边界框预测所需的计算速度和精度。在自建的无人机道路目标数据集上进行训练,结果表明,相较YOLOv5算法,YOLOv5-FCC算法可将mAP50和mAP50-95分别提高2.0%和4.2%。在VisDrone数据集上也验证了YOLOv5-FCC算法的有效性,并建立了基于无人机的道路目标检测系统,实现道路目标的自动检测。

Spatial-Modulated Physical-Layer Network Coding Based on Block Markov Superposition Transmission for Maritime Relay Communications

As an alternative to satellite communications,multi-hop relay networks can be deployed for maritime long-distance communications.Distinct from terrestrial environment,marine radio signals are affected by many factors,e.g.,weather conditions,evaporation ducting,and ship rocking caused by waves.To ensure the data transmission reliability,the block Markov superposition transmission(BMST)codes,which are easily configurable and have predictable performance,are applied in this study.Meanwhile,the physical-layer network coding(PNC)scheme with spatial modulation(SM)is adopted to improve the spectrum utilization.For the BMST-SMPNC system,we propose an iterative algorithm,which utilizes the channel observations and the a priori information from BMST decoder,to compute the soft information corresponding to the XORed bits constructed by the relay node.The results indicate that the proposed scheme outperforms the convolutional coded SM-PNC over fast-fading Rician channels.Especially,the performance can be easily improved in high spatial correlation maritime channel by increasing the memory m.

基于改进YOLOv7的输电铁塔塔基检测算法

输电塔作为整个电力传输系统最重要的组成部分之一,需要及时对输电塔进行检测保证塔基的稳固以保障后期的使用。针对无人机采集到的输电塔图像存在背景复杂、背景与目标塔基对比度低、小目标及塔基不完整等问题,提出了基于改进YOLOv7的输电塔塔基检测算法。首先,通过无人机采集不同地形地貌的输电塔图像,构建高质量数据集。然后,在原始YOLOv7的Backbone层中加入卷积注意力模块CBAM注意力机制,以提高输电塔塔基特征的提取能力。最后,引入WIoU v3代替原坐标损失函数CIoU,以提高目标检测任务的准确性和稳定性。在该数据集上,使用改进后的YOLOv7算法与目前主流的目标检测算法进行对比实验,实验结果中所提算法的mAP值高达99.93%,比原始YOLOv7提高2.19%,FPS值为37.125,满足实时检测需求,算法的整体性能较好。实验验证了所提算法在塔基检测上的可行性和有效性,为后续塔基区周围水土情况的研究奠定了基础。

基于MES−YOLOv5s的综采工作面大块煤检测算法

综采工作面的目标具有高速运动、多尺度、遮挡等特点,现有的目标检测算法存在精度低、模型占用的内存大、硬件依赖强等问题。针对上述问题,提出了一种基于MES−YOLOv5s的综采工作面大块煤检测算法。采用轻量化设计,将MobileNetV3作为主干网络,以减小模型占用的内存,提高CPU端的检测速度;在颈部网络添加高效多尺度注意力(EMA)模块,融合不同尺度的上下文信息,并进一步减少计算开销;采用SIoU损失函数代替CIoU损失函数,以提高训练速度和推理准确性。消融实验结果表明:MobileNetV3大幅减少了模型占用的内存和检测时间,但mAP损失严重;EMA模块和SIoU损失函数可在一定程度上恢复损失的精度,同时保证模型在CPU上具有较高的检测速度,满足煤矿井下目标实时检测需求。对比实验结果表明,与DETR,YOLOv5n,YOLOv5s,YOLOv7模型相比,MES−YOLOv5s模型综合性能最好,mAP为84.6%,模型占用的内存为11.2 MiB,在CPU端的检测时间为31.8 ms,在高速运动、多尺度、遮挡和多目标的工况环境下能够保持较高的召回率和精度。

基于改进的Yolov5的无人机图像小目标检测

为了解决无人机航拍图像小目标检测算法检测速度与精度无法兼顾的问题,在Yolov5的基础上,提出了针对于无人机图像小目标检测的Yolov5_GBCS算法。在新的算法中,添加一个额外的检测头,以便增强对小目标的特征融合效果;在主干网络中分别采用GhostConv卷积模块、GhostBottleneckC3模块替换部分Conv模块和C3模块用以提取丰富特征和冗余特征以提高模型效率;引入加权双向特征金字塔网络(bidirectional feature pyramid network,BiFPN)结构,用以提高对小目标的检测精度;在主干网络和颈部网络中引入轻量化的卷积块注意力模块(convolutional block attention module,CBAM),关注重要特征并抑制不必要的特征,增强小目标特征表达能力;使用Soft-NMS算法来替换NMS,因此降低了小目标在密集场景下的漏检率。通过在VisDrone2019数据集上的实验结果表明,集成了所有改进的方法后的Yolov5_GBCS算法,不仅提高了检测精度,而且有效地提高了检测速度,模型的mAP从38.5%提高到43.2%,检测速度也从53 f/s提高到59 f/s。Yolov5_GBCS算法可以有效地实现无人机航拍图像中小目标识别。

基于CBAM-YOLO腰椎间盘突出症MRI图像的自动诊断体系

目的腰椎间盘突出症(lumbar disc herniation,LDH)是脊柱退行性疾病的常见类型,可导致腰痛和下肢神经症状。MRI图像在诊断中至关重要,但存在依赖经验和缺乏标准化机制的问题。研究旨在开发基于卷积块注意力模块-视觉目标检测算法(convolutional block attention module-you only look once,CBAM-YOLO)的深度学习模型,以辅助自动诊断LDH MRI图像,从而提高诊断的准确性和效率。方法研究采纳了一个包含643例LDH患者MRI图像数据的公开数据集,每张图像均详细标注了包括椎间盘等在内的多种结构信息,并提出了一种新型模型CBAM-YOLO,它是在原有YOLO-v8模型的基础上,通过嵌入卷积注意力模块CBAM进行改良。此改进有助于模型更精确地识别突出椎间盘的特征位置及空间分布信息。为了充分验证模型的性能,研究使用CBAM-YOLO模型对经过数据增强的训练集进行了系统训练,共进行了100个训练周期。在模型评估环节,研究采用了Precision、Recall、F1-Score、Accuracy以及mAP等多个评价指标,以全面而严谨地评估模型的性能表现。结果CBAM-YOLO模型在诊断腰椎间盘突出症MRI影像方面展现出了卓越的性能。相较于原YOLO-v8模型,研究所构建的CBAM-YOLO模型在Precision达到了89.9%,Recall高达100.0%,F1-Score为94.6%,Accuracy为89.9%,以及mAP达到了97.1%,均表现出了明显的优势。结论测试结果充分凸显了基于深度学习的自动诊断体系在腰椎MRI图像中椎间盘识别与分割方面的巨大潜力,同时在临床应用中可提高疾病诊断的准确性和效率,进而减轻医疗专业人员的负担。

基于改进YOLOv5的Logo检测算法

针对Logo图像背景复杂、Logo目标尺寸多变的问题,提出了一种基于YOLOv5的改进检测算法。首先,结合CBAM(Channel Block Attention Module),分别在图像通道与空间方向进行压缩,提取图像的关键信息与重要区域;然后,使用可变空洞卷积(SAC)使网络在不同尺度下自适应地调整特征图中的感受野大小,以捕获不同尺度下的物体信息,改善网络对多尺度目标的检测效果;最后,将归一化Wasserstein距离(NWD)嵌入损失函数,将边界框建模成2D的高斯分布,计算对应的高斯分布之间的相似度,更好地度量目标之间的相似性,提高对小目标的检测性能与模型鲁棒性和稳定性。实验结果表明,在数据量较小的数据集FlickrLogos-32中,改进后算法的平均精度均值(mAP@0.5)达到90.6%,比原始YOLOv5算法提升了1个百分点;在数据量较大的数据集QMULOpenLogo中,改进后算法的mAP@0.5达到62.7%,比原始YOLOv5算法提升了2.3个百分点;在针对特定类型的Logo检测集LogoDet3K中,针对3类商标改进后算法比原始算法的mAP@0.5分别提升了1.2、1.4与1.4个百分点,说明它有更好的Logo图像小目标检测能力。

Differential modulation based on space-time block codes

A differential modulation scheme using space-time block codes is put forward. Compared with other schemes, our scheme has lower computational complexity and has a simpler decoder. In the case of three or four transmitter antennas, our scheme has a higher rate a higher coding gain and a lower bit error rate for a given rate. Then we made simulations for space-time block codes as well as group codes in the case of two, three, four and five transmit antennas. The simulations prove that using two transmit antennas, one receive antenna and code rate of 4 bits/s/Hz, the differential STBC method outperform the differential group codes method by 4 dB. Useing three, four and five transmit antennas, one receive antenna, and code rate of 3 bits/s/Hz are adopted, the differential STBC method outperform the differential group codes method by 5 dB, 6. 5 dB and 7 dB, respectively. In other words, the differential modulation scheme based on space-time block code is better than the corresponding differential modulation scheme

MAKING FULL USE OF OUTDATED CHANNEL ESTIMATES FOR BLOCK BY BLOCK ADAPTIVE MODULATIONS

This paper explores the potential to use accurate but outdated channel estimates for adaptive modulation. The work is novel in that the research is conditioned on block by block adaptation. First,we define a new quantity,the Tolerable Average Use Delay (TAUD),which can indicate the ability of an adaptation scheme to tolerate the delay of channel estimation results. We find that for the variable-power schemes,TAUD is a constant and dependent on the target Bit Error Rate (BER),average power and Doppler frequency; while for the constant-power schemes,it depends on the ad-aptation block length as well. At last,we investigate the relation between the delay tolerating per-formance and the spectral efficiency and give the system design criterion. The delay tolerating per-formance is improved at the price of lower data rate.

基于改进YOLOv5的镍板表面缺陷检测方法

针对镍板表面缺陷检测智能化程度低的问题,提出了一种基于改进YOLOv5的镍板表面缺陷检测方法。首先,对图像增强后的镍板数据集通过K-means++重新聚类锚框,提高锚框对本文数据集的适应度。其次,在主干网络Backbone中加入CBAM注意力机制,通过空间与通道信息融合来加强对感兴趣区域以及不清晰目标的特征识别。最后,在边界框回归时引入EIoU损失函数代替原CIoU损失函数,有效提高回归收敛速度,从而提高模型检测速度。实验结果表明,在自建的镍板缺陷数据集上,改进后的模型检测准确率高于Faster R-CNN、SSD、YOLOv3、YOLOv5等模型,其平均精度均值达81.4%,检测速度达61帧/s,模型在提高检测精度的同时也很好地满足了对检测速度的要求。

MoS_(2)核壳球上氧掺杂的动力学调制及其电化学分解水效应

在氩气气氛中900℃煅烧前驱物MoS_(2)核壳超级球得到MoS_(2)核壳球。在升温速率为20、10、5、2℃·min^(-1)时,MoS_(2)核壳球上氧掺杂量分别从前驱物的23.1%降低到17.6%、10.8%、5.5%、6.2%。结果表明较低的升温速率可以导致更低的氧掺杂量。基于前驱物特殊的准分子超晶格结构,提出了原位阴离子交换反应机理,以深入理解MoS_(2)核壳球上氧掺杂的动力学调制机理。电化学性能研究表明通过调制氧掺杂量可以有效改善MoS_(2)核壳球电化学分解水的性能。