基于改进YOLOv8模型的配电网设备检测方法OA

To address the challenges posed by diverse target shapes,significant occlusion,and complex environments in distribution network target detection,along with the slow detection speed and inadequate accuracy of existing target detection algorithms,an improved you only look once(YOLO)v8 algorithm for detecting low-voltage distribution network equipment is proposed.A multi-attention fusion block(MAFB)is constructed to refine feature fusion through a hierarchical processing pipeline,enhancing robustness and task adaptability.The original detection head undergoes lightweight processing,resulting in the design of a shared convolutional lightweight detection head that substantially reduces the number of parameters while resolving scale inconsistency issues.The bounding box regression loss function is optimized by integrating a multi-metric weighted loss function combining MPDIoU,Inner-IoU,and Wise-IoU.Experiments on the Roboflow dataset demonstrate that the improved model achieves a 7.0%reduction in parameters compared to YOLOv8m,while boosting mAP50 and mAP50:95 by 9.7 percentage points and 8.1 percentage points,respectively,validating the model's effectiveness.