IRMB-SWC-YOLO:defect detection algorithm for transmission lines based on improved YOLOv8

Abstract Transmission lines are an important component of the power transmission network, and their defects directly affect the safety and stability of the power system. This article proposes an improved YOLOv8 model based on the IRMB-SWC module to efficiently detect defects of transmission lines. Firstly, the C2f-iRMB-SWC module adopts the Concatenate to Fusion(C2f) structure of feature fusion, which enhances the integration ability of multi-level features and extracts richer feature information. Secondly, the module introduces the Improved Reception Field Multi Branch (iRMB) design, which extends the receptive field through a multi branch structure, enabling the model to capture more detailed local features and significantly improve its ability to detect complex defects. Then, by combining the Squeeze and Excitation (SE) mechanism with Sliding Window Convolution(SWC) operations, the model’s response to key features was further enhanced and feature representation was optimized. Finally, through experimental evaluation in the task of detecting defects in transmission lines, the improved YOLOv8 model showed a significant improvement in detection accuracy, with a 4.4% increase compared to the original YOLOv8 model. This validates the effectiveness of the IRMB-SWC module in practical applications and meets the accuracy requirements for wire defect detection.