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Pill Defect Detection Based on Improved YOLOv5s Network 被引量:1
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作者 AI Sheng CHEN Yitao +1 位作者 LIU Fang ZHU Aoxiang 《Instrumentation》 2022年第3期27-36,共10页
To address the problems of low detection accuracy and slow speed of traditional vision in the pharmaceutical industry,a YOLOv5s-EBD defect detection algorithm:Based on YOLOv5 network,firstly,the channel attention mech... To address the problems of low detection accuracy and slow speed of traditional vision in the pharmaceutical industry,a YOLOv5s-EBD defect detection algorithm:Based on YOLOv5 network,firstly,the channel attention mechanism is introduced into the network to focus the network on defects similar to the pill background,reducing the time-consuming scanning of invalid backgrounds;the PANet module in the network is then replaced with BiFPN for differential fusion of different features;finally,Depth-wise separable convolution is used instead of standard convolution to achieve the output Finally,Depth-wise separable convolution is used instead of standard convolution to achieve the output feature map requirements of standard convolution with less number of parameters and computation,and improve detection speed.the improved model is able to detect all types of defects in tablets with an accuracy of over 94%and a detection speed of 123.8 fps,which is 4.27%higher than the unimproved YOLOv5 network model with 5.2 fps. 展开更多
关键词 Pill Defect Detection Channel Attention Mechanism Differentiation fusion Depth-separable Convolution
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