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Among the existing insulator defect detection methods, the automatic detection of inspection robots based on the instance segmentation algorithm is relatively more efficient, but the problem of the limited accuracy of the segmentation algorithm is still a bottleneck for increasing inspection efficiency. Therefore, we propose a single-stage insulator instance defect segmentation method based on both an attention mechanism and improved feature fusion network. YOLACT is selected as the basic instance segmentation model. Firstly, to improve the segmentation speed, MobileNetV2 embedded with an scSE attention mechanism is introduced as the backbone network. Secondly, a new feature map that combines semantic and positional information is obtained by improving the FPN module and fusing the feature maps of each layer, during which, an attention mechanism is introduced to further improve the quality of the feature map. Thirdly, in view of the problems that affect the insulator segmentation, a Restrained-IoU (RIoU) bounding box loss function which covers the area deviation, center deviation, and shape deviation is designed for object detection. Finally, for the validity evaluation of the proposed method, experiments are performed on the insulator defect data set. It is shown in the results that the improved algorithm achieves a mask accuracy improvement of 5.82% and a detection speed of 37.4 FPS, which better complete the instance segmentation of insulator defect images.

The current existing problem of deep learning framework for the detection and segmentation of electrical equipment is dominantly related to low precision. Because of the reliable, safe and easy-to-operate technology provided by deep learning-based video surveillance for unmanned inspection of electrical equipment, this paper uses the bottleneck attention module (BAM) attention mechanism to improve the Solov2 model and proposes a new electrical equipment segmentation mode. Firstly, the BAM attention mechanism is integrated into the feature extraction network to adaptively learn the correlation between feature channels, thereby improving the expression ability of the feature map; secondly, the weighted sum of CrossEntropy Loss and Dice loss is designed as the mask loss to improve the segmentation accuracy and robustness of the model; finally, the non-maximal suppression (NMS) algorithm to better handle the overlap problem in instance segmentation. Experimental results show that the proposed method achieves an average segmentation accuracy of mAP of 80.4% on three types of electrical equipment datasets, including transformers, insulators and voltage transformers, which improve the detection accuracy by more than 5.7% compared with the original Solov2 model. The segmentation model proposed can provide a focusing technical means for the intelligent management of power systems. [ABSTRACT FROM AUTHOR]