Due to the small size of defects, partial occlusion, and cluttered background, insulator defect detection in transmission lines remains challenging. To address these issues, this paper proposes an improved Single Shot MultiBox Detector (SSD) framework. Firstly, a feature pyramid network is introduced for bidirectional multi-scale feature fusion to enhance the representation of small defects. Secondly, after fusing the feature maps, a convolutional block attention module is embedded to suppress background interference and highlight responses related to defects. Thirdly, focus loss replaces the original confidence loss to alleviate the imbalance of foreground and background during the training process. The proposed method achieved 99.03% insulator AP, 98.27% defect AP, and 98.65% mAP on a self-built dataset, which is 9.97 percentage points higher than the baseline SSD. The ablation study confirmed the complementary contributions of the three modules. The proposed detector significantly improves the detection reliability and robustness in complex detection scenarios, providing effective technical support for intelligent maintenance of transmission equipment.
Lv et al. (Mon,) studied this question.