Underwater Visual Multi-Target Tracking Algorithm Integrating Re-parameterization and Attention Mechanism

The complex underwater environment can severely impact the stability of imaging devices and the quality of captured images, posing significant challenges for visual multi-target tracking in underwater unmanned autonomous systems. To address the difficulties arising from underwater camera jitter and image degradation, this paper proposed an underwater visual multi-target tracking algorithm that integrated re-parameterization and attention mechanisms, specifically tailored for underwater unmanned autonomous systems. First, to tackle the diversity of underwater targets and image degradation, an improved YOLOv8 algorithm based on re-parameterization and attention mechanism(RA-YOLOv8) was proposed. This algorithm effectively enhanced the network’s multi-scale feature extraction capability and improved the detection accuracy of the model by integrating a structurally re-parameterized multi-scale feature extraction convolutional structure(DBB-RFAConv) and the AMSCE-attention mechanism. Then, to address the challenges of real-time target tracking caused by underwater camera jitter, an Inner-PIoUv2-enhanced ByteTrack algorithm(IP2-ByteTrack) was proposed. Inner-PIoUv2 was used as the similarity measure in the matching process of the tracking algorithm, which enhanced the model’s performance in underwater detection and tracking tasks, improving the accuracy of tracking trajectory matching. Finally, based on the RA-YOLOv8 and IP2-ByteTrack algorithms, an underwater visual multi-target tracking algorithm that integrated re-parameterization and attention mechanisms for underwater autonomous systems was proposed. Experimental results show that the proposed algorithm exhibits excellent performance in complex underwater environments and can effectively address the shortcomings of existing methods in underwater multi-target tracking.