Defect detection method of rubber seal ring based on improved YOLOv7-tiny
Aiming at the problem of low efficiency in traditional detection of surface defects of rubber seal rings,an improved YOLOv7-tiny algorithm for surface defect detection of rubber seal rings was proposed.The PConv optimized ELAN structure was introduced into the backbone feature extraction network to enhance the algorithm's feature extraction capability and to reduce the number of parameters.The global attention mechanism(GAM)was introduced into the feature fusion network,utilizing the attention weights between each pair of 3D channels,spatial widths,and spatial heights to improve efficiency by capturing the important features in three dimensions,thus enhancing the algorithm's feature fusion capability.The WIoU loss function was employed to optimize the original bounding box loss function,enhancing the algorithm's ability to locate the detected targets through a situation-compliant gradient gain allocation strategy.Additionally,a P2 small-target detection layer was added to strengthen the fusion of the deep and shallow feature information,thereby enhancing the algorithm's ability to detect small-target defects.Experimental comparisons were conducted using the O-Rings dataset.The improved algorithm was compared with the YOLOv7-tiny algorithm,resulting in a 7.8%improvement in mAP and achieving a detection accuracy of 90.9%,meeting the needs of actual industrial production.
YOLOv7-tinyrubber seal ringdefect detectionattention mechanismsmall target detection layer
NETL
NSTL
万方数据
