Abstract: This paper proposes a transmission line fitting defect detection method based on multiscale feature fusion attention and cross-layer aggregation,built upon the general framework ofthe YOLO series model.To address the issue of low detection accuracy of transmission line fittings in complex environments,a Multiscale Dual-branch Attention(MDA) mechanism is introduced into the feature extraction part of the network.This mechanism captures cross-dimensional interactions of multiscale features,establishing long-term dependencies between dimensions,thereby significantly improving detection performance.Additionally,to mitigate the loss of detail during feature transfer,a Cross-Layer Aggregation(CLA) module is proposed.This module aggregates multilevel feature layers from the backbone network with multilevel detection layers in the detection neck,preserving fine-grained information that might be lost during feature transmission.Compared to other state-of-the-art object detection models,the proposed method achieves higher detection accuracy on real-world transmission line fitting defect datasets,particularly excelling in small target defect detection and background noise suppression,demonstrating its practical value in transmission line maintenance.

Key words: Transmission line fittings, Defect detection, Multiscale features, YOLO, Attention mechanism, Feature fusion