针对电动自行车头盔佩戴检测存在小目标漏检、准确率低的问题,提出一种基于YOL Ov5s(You Only Look Once version 5 small)的改进电动车头盔检测算法。在主干网络中引入CBAM(Convolutional Block Attention Module)卷积注意力机制,以提...针对电动自行车头盔佩戴检测存在小目标漏检、准确率低的问题,提出一种基于YOL Ov5s(You Only Look Once version 5 small)的改进电动车头盔检测算法。在主干网络中引入CBAM(Convolutional Block Attention Module)卷积注意力机制,以提升对聚集目标的关注,解决因遮挡导致的检测效果差的问题;将颈部网络中的FPN(Feature Pyramid Network)+PAN(Path Aggregation Network)结构改为结合了跨尺度特征融合方法思想的特征融合结构,增强模型不同方向上的多尺度融合能力,使目标多尺度特征有效融合,提升对小目标的识别能力;使用SIoU(Structured Intersectionover Union)定位损失函数代替CIoU(Complete Intersection over Union)损失函数,以提高边框回归精度。实验结果表明,改进后的YOLOv5s模型准确率P和召回率R分别为94.7%和91.2%,平均精度值mAP为95.6%,相较于原始YOLOv5s模型分别提升6%、7%和6.5%。该方法使电动自行车头盔佩戴检测准确率得到了明显提升。展开更多
To solve the problems of the low accuracy and poor real-time performance of traditional strip steel surface defect detection meth-ods,which are caused by the characteristics of many kinds,complex shapes,and different ...To solve the problems of the low accuracy and poor real-time performance of traditional strip steel surface defect detection meth-ods,which are caused by the characteristics of many kinds,complex shapes,and different scales of strip surface defects,a strip steel surface defect detection algorithm based on improved Faster R-CNN is proposed.Firstly,the residual convolution module is inserted into the Swin Transformer network module to form the RC-Swin Transformer network module,and the RC-Swin Transformer module is introduced into the backbone network of the traditional Faster R-CNN to enhance the ability of the network to extract the global feature information of the image and adapt to the complex shape of the strip steel surface defect.To improve the attention of the network to defects in the image,a CBAM-BiFPN network module is designed,and then the backbone network is combined with the CBAM-BiFPN network to realize the de-tection and fusion of multi-scale features.The RoI align layer is used instead of the RoI pooling layer to improve the accuracy of defect loca-tion.Finally,Soft NMS is used to achieve non-maximum suppression and remove redundant boxes.In the comparative experiment on the NEU-DET dataset,the improved algorithm improves the mean average precision by 4.2%compared with the Faster R-CNN algorithm,and also improves the average precision by 6.1%and 6.7%for crazing defect and rolled-in scale defect,which are difficult to detect with the Faster R-CNN algorithm.The experiments show that the improvements proposed in the paper effectively improve the detection accuracy of the algorithm and have certain practical value.展开更多
文摘针对电动自行车头盔佩戴检测存在小目标漏检、准确率低的问题,提出一种基于YOL Ov5s(You Only Look Once version 5 small)的改进电动车头盔检测算法。在主干网络中引入CBAM(Convolutional Block Attention Module)卷积注意力机制,以提升对聚集目标的关注,解决因遮挡导致的检测效果差的问题;将颈部网络中的FPN(Feature Pyramid Network)+PAN(Path Aggregation Network)结构改为结合了跨尺度特征融合方法思想的特征融合结构,增强模型不同方向上的多尺度融合能力,使目标多尺度特征有效融合,提升对小目标的识别能力;使用SIoU(Structured Intersectionover Union)定位损失函数代替CIoU(Complete Intersection over Union)损失函数,以提高边框回归精度。实验结果表明,改进后的YOLOv5s模型准确率P和召回率R分别为94.7%和91.2%,平均精度值mAP为95.6%,相较于原始YOLOv5s模型分别提升6%、7%和6.5%。该方法使电动自行车头盔佩戴检测准确率得到了明显提升。
基金supported by the National Natural Science Foundation of China(12002138).
文摘To solve the problems of the low accuracy and poor real-time performance of traditional strip steel surface defect detection meth-ods,which are caused by the characteristics of many kinds,complex shapes,and different scales of strip surface defects,a strip steel surface defect detection algorithm based on improved Faster R-CNN is proposed.Firstly,the residual convolution module is inserted into the Swin Transformer network module to form the RC-Swin Transformer network module,and the RC-Swin Transformer module is introduced into the backbone network of the traditional Faster R-CNN to enhance the ability of the network to extract the global feature information of the image and adapt to the complex shape of the strip steel surface defect.To improve the attention of the network to defects in the image,a CBAM-BiFPN network module is designed,and then the backbone network is combined with the CBAM-BiFPN network to realize the de-tection and fusion of multi-scale features.The RoI align layer is used instead of the RoI pooling layer to improve the accuracy of defect loca-tion.Finally,Soft NMS is used to achieve non-maximum suppression and remove redundant boxes.In the comparative experiment on the NEU-DET dataset,the improved algorithm improves the mean average precision by 4.2%compared with the Faster R-CNN algorithm,and also improves the average precision by 6.1%and 6.7%for crazing defect and rolled-in scale defect,which are difficult to detect with the Faster R-CNN algorithm.The experiments show that the improvements proposed in the paper effectively improve the detection accuracy of the algorithm and have certain practical value.
