A Real-Time Shipping Container Accident Inference System Monitoring the Alignment State of Shipping Containers in Edge Environments

Abstract

Along with the recent development of artificial intelligence technology, convergence services that apply technology are undergoing active development in various industrial fields. In particular, artificial intelligence-based object recognition technologies are being widely applied to the development of intelligent analysis services based on image data and streaming video data. As such, in the port yard, these object recognition technologies are being used to develop port safety services in smart ports. Accidents are a frequent occurrence in port yards due to misaligned loading of ship containers. In order to prevent such accidents, various studies using artificial intelligence technology are underway. In this paper, we propose a real-time shipping container accident inference edge system that can analyze the ship container’s loading status from a safety point of view to prevent accidents in advance. The proposed system includes the collection of video data of the ship container, inferring the safety level of the alignment status of the ship container, and transmitting the inference results for the safety level. In this paper, the proposed inference model is implemented with YOLOv3, YOLOv4 and YOLOv7 networks and can be used in video monitoring to realize the accurate classification and positioning of three different safety levels (safe, caution, and danger) in real time. In the performance evaluation, the detection accuracy of the inference model implemented with the YOLOv4 network was greater than 0.95. Its performance was also significantly better than that of the inference model implemented with the YOLOv3 and YOLOv7 networks. Although it was slightly inferior to the YOLOv4 network in terms of the accuracy, the inference model implemented with the YOLOv3 network had a faster inference speed than the model implemented with the YOLOv4 and YOLOv7 networks. Because of the port safety scenario, in which the inference accuracy is more important than the inference speed, we applied the YOLOv4 algorithm to the inference model of the system.