A Hybrid Excitation Model Based Lightweight Siamese Network for Underwater Vehicle Object Tracking Missions

作者：Xiaofeng Wu, Xinyue Han, Zongyu Zhang, Han Wu, Xu Yang, Hai Huang · 发表于：Journal of Marine Science and Engineering · 年份：2023 · DOI：10.3390/jmse11061127 · 被引用次数：10 · 研究领域：Video Surveillance and Tracking Methods、Fire Detection and Safety Systems、Infrared Target Detection Methodologies

Performing object tracking tasks and efficiently perceiving the underwater environment in real time for underwater vehicles is a challenging task due to the complex nature of the underwater environment. A hybrid excitation model based lightweight Siamese network is proposed to solve the mismatch between underwater objects with limited characteristics and complex deep learning models. The lightweight neural network is applied to the residual network in the Siamese network to reduce the computational complexity and cost of the model while constructing a deeper network. In addition, to deal with the changeable complex underwater environment and consider the timing of video tracking, the global excitation model (HE module) is introduced. The model adopts the excitation methods of space, channel, and motion to improve the accuracy of the algorithm. Based on the designed underwater vehicle, the underwater target tracking and target grabbing experiments are carried out, and the experimental results show that the proposed tracking algorithm has a high tracking success rate.