An Efficient Detector for the Key Components of the Power Transmission Lines

Accurate detection of the key components of transmission lines is an important part of smart grid construction. However, the detection of key components of transmission lines faces the problems of severe occlusion, irregular shape, and large size differences, which present a great challenge for anchor-based object detectors. We propose the anchor-based and anchor-free (ABAF) model, an object detection algorithm for both general and special object datasets. The ABAF detector is jointly trained with the anchor-based branch and anchor-free branch. At the time of inference, the predicted results of both are fused to yield the final detections. The experimental results show that the anchor-based branch is good at detecting shape-regular objects and the anchor-free branch is better at detecting irregularly shaped objects than anchor-based branch, and our fusion model ABAF has strong robustness for different datasets with excellent and stable performance. ABAF with ResNet-50 achieves 89.69% mAP on the transmission line dataset, a 3.72% improvement over one-stage anchor-based detector RetinaNet and we achieve 79.03% mAP on the PASCAL VOC dataset.