随着电子设备的广泛应用,印刷电路板(Printed Circuit Board,PCB)在电子制造行业中具有重要意义.然而,由于制造过程中的不完美和环境因素的干扰,PCB上可能存在微小的缺陷.因此,开发高效准确的缺陷检测算法对于确保产品质量至关重要.针对...随着电子设备的广泛应用,印刷电路板(Printed Circuit Board,PCB)在电子制造行业中具有重要意义.然而,由于制造过程中的不完美和环境因素的干扰,PCB上可能存在微小的缺陷.因此,开发高效准确的缺陷检测算法对于确保产品质量至关重要.针对PCB微小缺陷检测问题,本文提出了一种基于多维注意力机制的高精度PCB微小缺陷检测算法.为降低网络的模型参数量和计算量,引入部分卷积(Partial Convolution,PConv),将ELAN(Efficient Layer Aggregation Network)模块设计为更加高效的P-ELAN,同时,为增强网络对微小缺陷的特征提取能力,引入多维注意力机制(Multi-Dimensional Attention Mechanism,MDAM)的全维动态卷积(Omni-dimensional Dynamic Convolution,ODConv)并结合部分卷积,设计了POD-CSP(Partial ODconv-Cross Stage Partial)和POD-MP(Partial ODconv-Max Pooling)跨阶段部分网络模块,提出了OD-Neck结构.最后,本文基于(Youo Only Look Once version 7,YOLOv7)提出了对小目标更加高效的YOLO-POD模型,并在训练阶段采用一种新颖的Alpha-SIoU损失函数对网络进行优化.实验结果表明,YOLO-POD的检测精确率和召回率分别达到了98.31%和97.09%,并在多个指标上取得了领先优势,尤其是对于更严格的(mean Average Precision at IoU threshold of 0.75,mAP75)指标,比原始的YOLOv7模型提高28%.验证了YOLO-POD在PCB缺陷检测性能中具有较高的准确性和鲁棒性,满足高精度的检测要求,可为PCB制造行业提供有效的检测解决方案.展开更多
In order to address the issues of the current sheep face pain detection algorithm under complex environments with poor detection accuracy and complex models,this paper proposes a two-stage method of sheep face pain de...In order to address the issues of the current sheep face pain detection algorithm under complex environments with poor detection accuracy and complex models,this paper proposes a two-stage method of sheep face pain detection based on light yolov5s.First,the weights of the yolov5s model are reduced by combining the Ghostnet structure,feature fusion is performed using the BiFPN structure and the ODConv join to improve detection accuracy.The experimental results show that the number of parameters and complexity of the optimized model are reduced by 38.03%and 50.94%,respectively,compared with the original model,and the accuracy of recognizing sheep faces is improved by 0.2%and the recall rate is increased by 1%.Compared to current mainstream algorithms such as yolov4 tiny and SSD,it not only significantly reduces the number of parameters but also has a better detection performance.Second,pain detection of sheep faces recognised by lightweight yolov5s was carried out by MobileNetV2,and experimental results showed that MobileNetV2 achieved a mean classification accuracy of over 98%for painful sheep,and this illustrates that this two-stage sheep face pain recognition research method has great application value for sheep health breeding.展开更多
文摘随着电子设备的广泛应用,印刷电路板(Printed Circuit Board,PCB)在电子制造行业中具有重要意义.然而,由于制造过程中的不完美和环境因素的干扰,PCB上可能存在微小的缺陷.因此,开发高效准确的缺陷检测算法对于确保产品质量至关重要.针对PCB微小缺陷检测问题,本文提出了一种基于多维注意力机制的高精度PCB微小缺陷检测算法.为降低网络的模型参数量和计算量,引入部分卷积(Partial Convolution,PConv),将ELAN(Efficient Layer Aggregation Network)模块设计为更加高效的P-ELAN,同时,为增强网络对微小缺陷的特征提取能力,引入多维注意力机制(Multi-Dimensional Attention Mechanism,MDAM)的全维动态卷积(Omni-dimensional Dynamic Convolution,ODConv)并结合部分卷积,设计了POD-CSP(Partial ODconv-Cross Stage Partial)和POD-MP(Partial ODconv-Max Pooling)跨阶段部分网络模块,提出了OD-Neck结构.最后,本文基于(Youo Only Look Once version 7,YOLOv7)提出了对小目标更加高效的YOLO-POD模型,并在训练阶段采用一种新颖的Alpha-SIoU损失函数对网络进行优化.实验结果表明,YOLO-POD的检测精确率和召回率分别达到了98.31%和97.09%,并在多个指标上取得了领先优势,尤其是对于更严格的(mean Average Precision at IoU threshold of 0.75,mAP75)指标,比原始的YOLOv7模型提高28%.验证了YOLO-POD在PCB缺陷检测性能中具有较高的准确性和鲁棒性,满足高精度的检测要求,可为PCB制造行业提供有效的检测解决方案.
文摘In order to address the issues of the current sheep face pain detection algorithm under complex environments with poor detection accuracy and complex models,this paper proposes a two-stage method of sheep face pain detection based on light yolov5s.First,the weights of the yolov5s model are reduced by combining the Ghostnet structure,feature fusion is performed using the BiFPN structure and the ODConv join to improve detection accuracy.The experimental results show that the number of parameters and complexity of the optimized model are reduced by 38.03%and 50.94%,respectively,compared with the original model,and the accuracy of recognizing sheep faces is improved by 0.2%and the recall rate is increased by 1%.Compared to current mainstream algorithms such as yolov4 tiny and SSD,it not only significantly reduces the number of parameters but also has a better detection performance.Second,pain detection of sheep faces recognised by lightweight yolov5s was carried out by MobileNetV2,and experimental results showed that MobileNetV2 achieved a mean classification accuracy of over 98%for painful sheep,and this illustrates that this two-stage sheep face pain recognition research method has great application value for sheep health breeding.
