Transmission Line Object Detection Method Based on Label Adaptive Allocation

Abstract

Inspection of the integrality of components and connecting parts is an important task to maintain safe and stable operation of transmission lines. In view of the fact that the scale difference of the auxiliary component in a connecting part is large and the background environment of the object is complex, a one-stage object detection method based on the enhanced real feature information and the label adaptive allocation is proposed in this study. Based on the anchor-free detection algorithm FCOS, this method is optimized by expanding the real feature information of the adjacent feature layer fusion and the semantic information of the deep feature layer, as well as adaptively assigning the label through the idea of pixel-by-pixel detection. In addition, the grading ring image is sliced in original data to improve the proportion of bolts in the dataset, which can clear the appearance features of small objects and reduce the difficulty of detection. Experimental results show that this method can eliminate the background interference in the GT (ground truth) as much as possible in object detection process, and improve the detection accuracy for objects with a narrow shape and small size. The evaluation index AP (average precision) increased by 4.1%. Further improvement of detection accuracy lays a foundation for the realization of efficient real-time patrol inspection.

Foreign Objects Identification of Transmission Line Based on Improved YOLOv7

“…Literature [48] proposes a YOLOv5 method for transmission line component detection, while this paper applies the latest YOLO version to the identification of transmission line hazards, which is equivalent to supplementing and improving this literature. While literature [49] applies FCOS to transmission line inspection, this paper's YOLOv7 draws on the idea of FCOS to improve the model recognition accuracy by means of multi-scale target detection. In this paper, SPD convolution is added to YOLOv7, while the hyperparameters are optimized using a genetic algorithm.…”