Rotating Object Detection for Cranes in Transmission Line Scenarios

Abstract

Cranes are pivotal heavy equipment used in the construction of transmission line scenarios. Accurately identifying these cranes and monitoring their status is pressing. The rapid development of computer vision brings new ideas to solve these challenges. Since cranes have a high aspect ratio, conventional horizontal bounding boxes contain a large number of redundant objects, which deteriorates the accuracy of object detection. In this study, we use a rotating target detection paradigm to detect cranes. We propose the YOLOv8-Crane model, where YOLOv8 serves as a detection network for rotating targets, and we incorporate Transformers in the backbone to improve global context modeling. The Kullback–Leibler divergence (KLD) with excellent scale invariance is used as a loss function to measure the distance between predicted and true distribution. Finally, we validate the superiority of YOLOv8-Crane on 1405 real-scene data collected by ourselves. Our approach demonstrates a significant improvement in crane detection and offers a new solution for enhancing safety monitoring.