Enhanced Random Crop with Data Augmentation for Insulator Detection in Complex Backgrounds

With the development of the automatic inspection of unmanned aerial vehicles (UAVs), improving the detection accuracy of insulators will not only help further insulator state detection and fault diagnosis but also contribute to the early landing of the UAVs' automatic inspection system. In this paper, we propose a data augmentation method based on the random crop to improve the detection accuracy of insulators. Firstly, it ensures the validity of the label by generating a patch that contains the centers of all ground truth boxes. Secondly, it achieves a balance between protecting the ground truth and random cropping by limiting the area ratio of each ground truth box before and after random cropping. We find that these two steps increase the attention of the model to the insulator. On the self-made insulator dataset, the solution achieves 91.2% and 89.3% mAP in YOLOv3 and RetinaNet respectively, which is 3% and 1.5% better than the random crop.