SAC-YOLO: Efficient Multi-Scale Feature Fusion for Transmission Line Defect Detection

Transmission line inspection requires accurate and real-time detection of small defects in complex environments. However, targets such as broken lines, broken insulators, stained insulators, and nests are often small and easily affected by background interference. To address these challenges, this paper proposes an improved YOLOv11n-based detection method. First, the SPPF module is replaced with the AIFI module, which we introduce to enhance intra-scale feature interaction and global contextual modeling on high-level semantic features, thereby improving the representation of small defect targets with limited computational overhead. Second, a C3K2-CFBlock module is constructed by reformulating the feature extraction structure of the original C3K2, enabling efficient integration of local convolutional features and global contextual information for improved long-range dependency modeling with low computational cost. In addition, a Semantic-Guided Multi-Scale Fusion Module (SGMF) is proposed to address the inherent limitations of conventional FPN-based feature aggregation. Instead of relying on direct concatenation or element-wise addition, SGMF introduces channel-aware reweighting and a bidirectional guided fusion mechanism to establish explicit semantic interaction between feature levels, significantly improving the detection performance of small defect targets. Experimental results show that the proposed method improves Recall by 3.5%, and mAP@0.5:0.95 by 3.8% compared with YOLOv11n, the model achieves real-time inference at 277 FPS, which is comparable to the baseline performance, while maintaining a lightweight structure suitable for real-time deployment on edge devices.