An Optimized Algorithm for Transmission Line Anomaly Detection Based on Improved YOLOv11n

To address the issues of low accuracy in transmission line anomaly detection and recognition caused by challenges such as multi-scale targets and partial occlusion, this paper proposes an optimized transmission line anomaly detection algorithm based on improved YOLOv11n. Firstly, an improved Cross Stage Partial with kernel size 2 (C3k2_DFF) module takes the place of the original C3k2 module in the backbone network. It adaptively fuses multi-scale local features and dynamically selects salient channel-wise and spatial features according to its global information during fusion to enhance the model’s feature representation. Secondly, a Separated and Enhancement Attention Module (SEAM) attention mechanism is introduced to enhance the unoccluded area feature response to compensate for the occluded area response deficit, suppressing the background features that interfere with the model and improving the model’s occluded target perception capability. Experimental results on our self-constructed dataset indicate that the proposed improved YOLOv11n model achieves precision, recall, mAP50, and mAP50-95 of 94.2%, 90.8%, 94.3%, and 68.0%, respectively. Compared with the baseline model, it represents improvements of 2.0%, 1.3%, 1.6%, and 2.9%, while the parameters and GFLOPs increase by only 6.6% and 4.8%, demonstrating superior detection performance.