A Novel Vision-Based Truck-Lifting Accident Detection Method for Truck-Lifting Prevention System in Container Terminal

Abstract

Truck-lifting accidents are common in container-lifting operations. The most common truck-lifting prevention system is based on lidar scanners which are very expensive, and it is more promising to use cameras to detect truck-lifting accidents than to use lidar scanners. However, little work has been conducted on visual detection of truck-lifting accidents, and the only previous visual approach fails in the common scenario where wheel hubs in images appear as non-standard or incomplete circles. To this end, this paper proposes a novel vision-based truck-lifting accident detection method for truck-lifting prevention system, which is free from the disturbance of distorted or incomplete wheel hubs in the image. The main idea of the proposed method is to utilize a deep learning-based object detection model to detect the truck body within which to extract many key-points whose vertical displacements are tracked to determine whether the truck is lifted. Based on this idea, the workflow for truck-lifting accident detection is delicately constructed. In addition, a YOLOv5-based modified detection model is proposed to reduce the computation cost of container and truck body detection, achieving 38.5% increase in inference speed on a single industrial personal computer without performance decrease. The experimental results demonstrate that the proposed truck-lifting accident detection method is capable of accurately recognizing the truck-lifting operations with the recall rate of 100% and the false alarm rate of 0.42%.