Defect detection method of lithium battery electrode based on improved YOLOv5
The DDCNet-YOLO algorithm model was proposed based on the deformable convolution and YOLOv5,aiming at the complex lithium battery electrode surface with multiple small object defects and large aspect ratio object defects at the same time.The deformable downsampling convolution network(DDCNet)was constructed in the backbone.The context augmentation module(CAM)was introduced in the feature fusion part and the deformable convolution block(DCB)was used to replace the C3 module.AD-Head,a decoupling head with an attention mechanism,was designed in the head part.The RIoU method was proposed to optimize the loss calculation for different aspect ratio objects.Experiments showed that the DDCNet-YOLO model improved the mAP50 by 6.2 percentage points compared to YOLOv5s model and by 3.7 percentage points compared to YOLOv5m model.The lightweight model DDCNet-YOLOs,constructed by DDCNet and a decoupling head with an attention mechanism.The DDCNet-YOLOs improved the mAP50:95 by 8.9 percentage points and reduced the number of parameters by 7.2 percentage points,compared with the YOLOv5s model.In addition,both models were deployed based on the C++.The two algorithmic models focus on accuracy and speed respectively,but both can achieve high accuracy under the condition of meeting the actual detection speed requirement.
electrode defectdeformable convolutionsmall objectlarge aspect ratio objectYOLOv5
NETL
NSTL
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