A Fusion Algorithm of Object Detection and Tracking for Unmanned Surface Vehicles

Zhiguo Zhou, Xinxin Hu, Zeming Li, Zhao Jing, Chong Qu

Abstract: In order to provide reliable input for obstacle avoidance and decision-making, Unmanned Surface Vehicles (USV) need to have the function of sensing the position of other USV targets in the process of cooperation and confrontation. Due to the small size of the target and the interference of the water and sky background, the current algorithms are prone to missed detection and drift problems when detecting and tracking USV. Therefore, in this paper, we propose a fusion algorithm of detection and tracking for USV targets. To solve the problem of vague features in single-frame image, high-resolution and deep semantic information is obtained through a cross-stage partial network, and the anchor and convolution structure in the network has been improved in view of the characteristics of USV; Besides, in order to meet the real-time requirements, the detected target is quickly tracked through correlation filtering and the correlation characteristics of multi-frame images are obtained; Then, the correlation characteristics are used to significantly reduce missed detection and the tracking drift problems are corrected combined with high-resolution semantic features of single-frame. Finally, the fusion algorithm is designed. In this paper we construct a picture dataset and a video dataset to test the effect of detection, tracking, and fusion algorithm separately, which proves the superiority of the fusion algorithm in this paper. The results show that compared with a single detection algorithm and tracking algorithm, the fusion one can increase the success rate by more than 10%.

External IDs:dblp:journals/finr/ZhouHLJQ22