A Surveillance Video Real-Time Object Detection System Based on Edge-Cloud Cooperation in Airport Apron

Abstract

Airport apron carries a lot of preparations for flight operation, and the advancement of its various tasks is of great significance to the flight operation. In order to build a more intelligent and easy-to-deploy airport apron operation analysis guarantee system, it is necessary to study a low-cost, fast, and real-time object detection scheme. In this article, a real-time object detection solution based on edge cloud system for airport apron operation surveillance video is proposed, which includes lightweight detection model Edge-YOLO, edge video detection acceleration strategy, and a cloud-based detection results verification mechanism. Edge-YOLO reduces the amounts of parameters and computational complexity by using model lightweight technology, which can achieve better detection speed performance on edge-end embedded devices with weak computing power, and adds an attention mechanism to compensate for accuracy loss. Edge video detection acceleration strategy achieves further detection acceleration for Edge-YOLO by utilizing the motion information of objects in the video to achieve real-time detection. Cloud-based detection results verification mechanism verifies and corrects the detection results generated by the edge through a multi-level intervention mechanism to improve the accuracy of the detection results. Through this solution, we can achieve reliable and real-time monitoring of airport apron video on edge devices with the support of a small amount of cloud computing power.