Abstract: Due to variable scale and complex background of smoke fire targets in transmission line mountain fires,it may lead to low detection accuracy and false alarms.To address these issues,this paper proposes an improved YOLOv5 object detection algorithm.To tackle the problem of scale variability,aiming at the problem that SPPF cascade structure only focuses on local feature information during scale fusion,this paper proposes spatial pyramid pooling cross stage partial conva(SPPCSPC),which combines hierarchical and cross stage partial networks(CSP) structures,effectively extracting and fusing multi-level and different scale global feature information to enhance the model's ability to detect smoke and fire targets.Secondly,to solve the misdetection problem,this paper proposes a new neck network PCANet(path coordinate aggregation network).While integrating the backbone network and neck feature maps,it integrates the target's position information from the vertical and horizontal directions of the feature maps into the channels respectively.This enhances the model's attention to smoke fire target position information and reduces interference from complex backgrounds.Experiments are conducted on a transmission line smoke fire dataset to evaluate the proposed model.The proposed algorithm achieves an increase of 1.6% in mAP₅₀,1.5% in mAP_50:95,and 2.4% in recall,respectively,which effectively improve the detection accuracy of smoke and fire target,reduce the occurrence of false detection,and can better complete the detection task.

Key words: Power transmission line, Smoke and fire detection, Multi-scale feature fusion, Coordinate information, YOLOv5