基于改进YOLOv8的绝缘子自爆缺陷检测

Self-explosion defect detection of insulator based on improved YOLOv8

针对现有绝缘子自爆缺陷检测方法在复杂背景和雾天环境下存在检测精度低、易误检和漏检问题,提出一种基于改进YOLOv8的绝缘子自爆缺陷检测算法。首先,在主干网中引入用于低分辨率图像和小目标检测的SPD-Conv模块,充分提取绝缘子缺陷目标的特征信息;其次,在特征融合层将BiFPN与SimAM注意力机制结合构建BiFPN_SimAM模块替换PANet的concat连接,实现多尺度特征融合,提高网络整体性能。实验结果表明,改进后的算法对绝缘子自爆缺陷检测的精确率和mAP@0.5分别达到了95%和93.1%,较原YOLOv8算法分别提高了1.8%和1.5%,在复杂背景和雾天环境下对绝缘子自爆缺陷检测有较好的检测效果。

To address the problems of low accuracy,easy false detection and missed detection in the existing insulator self-explosion defect detection methods under complex backgrounds and foggy environments,an improved YOLOv8 insulator self-explosion defect detection algorithm is proposed.First,the SPD-Conv module for low resolution image and small target detection is introduced into the backbone network to fully extract the feature information of insulator defect target.Secondly,BiFPN is integrated with the SimAM attention mechanism to build the BiFPN_SimAM module,replacing the concat connection of PANet to achieve multi-scale feature fusion and enhance the overall performance of the network.The experimental results show that the precision and mAP@0.5 of the improved algorithm for insulator self-explosion defect detection reach 95%and 93.1%,respectively,which are increased by 1.8%and 1.5%compared with the original YOLOv8 algorithm,and it also has a good detection effect on insulator self-explosion defect detection under complex background and foggy environment.