Abstract：When performing real-time detection of insulator low-value defects in the field, one is often faced with a complex infrared image background, which is affected by a variety of factors. This leads to the existence of multi-scale and multi-angle characteristics of the captured small target hotspots. And in order to be applicable to the edge end, it is necessary to maintain a balance between accuracy and lightweighting. In this paper, to address the above problems, the small targets in complex environments YOLO (SCE-YOLO) algorithm is proposed. The SCE-YOLO algorithm is based on the YOLOv8 algorithm for improvement, and firstly, it uses the self-attention and convolution mix model. Attention and convolution mix (ACmix) to aggregate convolution and self-attention to increase the algorithm’s attention to the insulators, thus reducing the influence of the background on the algorithm. Secondly, the path aggregation network (PANet) is improved by cross-modal contextual feature module (CCFM) to integrate insulator features at different scales and reduce the computational effort of the model. Then the dynamic head (DyHead) is introduced and the second generation of deformable convolutional networks is improved into the third generation of deformable convolutional networks v3 (DCNV3), which significantly improves the expressive ability of the model’s target detecting head and further improves the algorithm’s ability to detect small target defects in complex environments. The algorithm’s ability to detect small target defects in complex environments is further improved by improving DCNv3. The improvement of DCNv3 also makes the model more computationally efficient. Finally, the Inner-SIoU loss function is used to replace the CIoU loss function, and the accuracy of the bounding box regression is improved by introducing an auxiliary bounding box, which improves the recall and accuracy of the detection of insulators with small targets, and enhances the robustness of the model for small targets. Meanwhile, relevant experiments are carried out to enrich the samples of insulator low-value defect dataset. Different low-value insulators were placed in different positions, and then the humidity and shooting angle were changed to obtain infrared images of low-value insulators heating under different environmental conditions. Based on this dataset, experiments are carried out to evaluate the accuracy and robustness of the algorithm and compare it with other typical algorithms. After experimental verification, the SCE-YOLO algorithm proposed in this paper achieves a detection average accuracy of 0.874 5 while the GFLOPs are reduced to 7.5, which meets the requirements of real-time detection of low-value defects of small-target insulators in the field, and proves the validity and superiority of the algorithm proposed in this paper through the experiments such as ablation and comparison.
裴少通, 王玮琦, 胡晨龙, 吴免霄, 孙海超. 基于小目标复杂环境YOLO算法的瓷质绝缘子低值缺陷识别方法[J]. 电工技术学报, 2025, 40(23): 7793-7805.
Pei Shaotong, Wang Weiqi, Hu Chenlong, Wu Mianxiao, Sun Haichao. Identification of Low-Value Defects in Porcelain Insulators Based on the Small Targets in Complex Environments YOLO Algorithm. Transactions of China Electrotechnical Society, 2025, 40(23): 7793-7805.
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