改进YOLOv7的输电线路融冰刀闸状态识别方法

State Recognition Method of Transmission Line Ice-Melting Switch Based on Improved YOLOv7

隔离刀闸状态的自动识别是冰期输电线路智能融冰倒闸操作中的关键环节。针对恶劣天气条件下,传统图像识别方法在识别融冰刀闸时精度较低的问题,提出了一种基于改进YOLOv7的融冰刀闸状态识别方法。在YOLOv7网络中引入自注意力机制(self-attention,S-A)模块,以增强网络在低对比度图像中的全局特征提取能力。同时对网络中的SPPCSPC模块进行改进,引入空洞空间金字塔池化技术(atrous spatial pyramid pooling,ASPP),提高对搭接刀闸等此类大目标的识别能力。根据搭接刀闸的特殊结构、大小和位置,在损失函数中添加约束项,增强对刀闸识别的针对性。最后,设计了一个M-MBO加速网络,利用多分支架构在推理时简化模型,提高模型识别速度。实验结果表明,在保证识别速度的同时,改进的YOLOv7模型mAP值可达97.9%,相比改进前的方法平均精度均值提高了2.5个百分点,验证了该方法的有效性。

Automatic identification of the state of disconnecting switch is deemed crucial for the intelligent ice-melting reversal operation of transmission lines during ice periods.Acknowledging the low accuracy of traditional image recogni-tion methods when identifying ice-melting switches under severe weather conditions,a method predicated on an improved YOLOv7 framework for recognizing the state of ice-melting switches is proposed.A self-attention(S-A)mechanism is integrated into the YOLOv7 network to enhance global feature extraction capability in low-contrast images.Concurrently,the SPPCSPC module in the network is modified with the inclusion of the atrous spatial pyramid pooling(ASPP)tech-nique,aimed at augmenting the recognition capability for larger targets such as the overlapped switch.Given the distinct structure,size,and position of the overlapped switch,constraint terms are introduced to the loss function to enhance its recognition specificity.An M-MBO acceleration network is subsequently designed,utilizing a multi-branch architecture to simplify the model during inference,thereby improving recognition speed.Experimental results reveals that the refined YOLOv7 model achieves a mean average precision(mAP)value of 97.9%,marking an increase of 2.5 percentage points in average precision compared to previous methods,underscoring the effectiveness of the proposed approach.