基于改进YOLOv7的输电线路绝缘子识别检测研究

Research on identification and detection of transmission line insulators based on improved YOLOv7

针对绝缘子目标尺寸小导致检测精度低、误检漏检率高的问题,提出一种基于YOLOv7改进的输电线路绝缘子检测模型。首先,将双支路融合通道注意力机制与主干部分的ELAN模块进行融合,强调重要的通道信息,抑制噪声等无用信息的干扰;其次,在特征融合部分加入局部自注意力机制,使得局部微小区域局部关注度增强;同时,在Neck部分融入BiFPN跨层连接,在增加部分计算量的同时,使得边缘信息得到更好的保留,更利于小目标的检测;最后,以精确度、召回率、平均精度均值等作为评价指标,对采集的数据集进行了消融实验和对比实验。实验结果表明,改进后的网络模型对输电线路绝缘子检测精度为92.1%,相比于传统的YOLOv7网络模型提高3%,并且其平均检测均值、召回率分别提高3.1%、3.6%;同时,改进的模型在各个评估指标上相比YOLOv5-ECA和Faster-R-CNN等均有显著优势,针对输电线路绝缘子检测具有良好效果。

Addressing the issue of low detection accuracy and high false positive and false negative rates caused by the small size of insulator targets,a transmission line insulator detection model based on the improved YOLOv7 is proposed.Firstly,the dual-branch fused channel attention mechanism is integrated with the main ELAN(encoder-local aggregation network)module to emphasize crucial channel information and suppress interference from noise and irrelevant data.Secondly,a locally self-attentive mechanism is introduced in the feature fusion section to enhance the focus on local tiny regions.Additionally,the BiFPN(Bi-directional feature pyramid network)cross-layer connection is incorporated in the Neck section to preserve edge information better and improve the detection of small targets while slightly increasing computational load.Lastly,using evaluation metrics such as precision,recall,and mean average precision,ablation experiments and comparative experiments are conducted on collected datasets.Experimental results indicate that the improved network model achieves a detection accuracy of 92.1%for transmission line insulator detection,a 3%enhancement over the traditional YOLOv7 network model.The average detection mean,recall rate,are also improved by 3.1%and 3.6%respectively.Furthermore,the improved model demonstrates significant advantages over YOLOv5-ECA and Faster R-CNN in various evaluation metrics,proving its effectiveness in detecting transmission line insulators.