A needle insulator inspection method based on bipolar detection algorithm

To ensure the safe and reliable operation of transmission lines, it is particularly important to monitor the status of key equipment such as insulators online and maintain them in a timely manner. As a key component of line insulation, the quality of pin insulators may lead to serious accidents. This article proposes an algorithm based on bipolar detection to address the problem of identifying ceramic pillar type porcelain insulators, composite insulators, and ceramic pin type porcelain insulators in existing algorithms. The algorithm first uses an improved YOLOv8 object detection network to detect the insulators mentioned above, and the backbone network introduces efficient modules such as Focus, CBM, CSP, etc.; The detection head adopts a PANet structure to fuse multi-level features. Then, the presence of the top area of the ceramic pin type porcelain insulator in the area of interest is judged. Only when both are detected simultaneously, can it be considered a true pin type insulator. On a self-built dataset of 110000 images, the improved algorithm improved the accuracy of pin insulator recognition from 88.9% to 96.3% and the recall rate from 90.1% to 97.2% compared to the original YOLOv8. The dual level identification strategy can effectively eliminate interference from other types of insulators and significantly reduce the false detection rate. The algorithm runs at a speed of 126fps, which can meet real-time detection requirements. In addition, the paper also introduces the details of algorithm training, model deployment, and Docker containerization deployment. Experimental results have shown that this method can accurately and efficiently detect line pin insulators and has high practical value for application scenarios such as drone inspections.