A Feature-Enhanced and Adaptive Routing Framework for Fish School Detection On AUVs for Degraded Underwater Imaging Environments

Underwater biological detection technology holds considerable promise for delving into the abundance of marine species and resources. It can seamlessly integrate into Autonomous Underwater Vehicles (AUVs), providing robust support for the application and enhancement of the Internet of Underwater Things (IoUT) systems. Recently, data-driven artificial intelligence technologies have exhibited considerable potential in underwater object detection. Nonetheless, the degraded underwater imaging environments present challenges for imaging devices on AUVs or other underwater devices. In this paper, we propose an advanced fish school detection framework designed to significantly contribute to the IoUT systems. This framework enhances performance in challenging conditions like inadequate illumination, blurriness, and high fish population density. The proposed framework demonstrates the capability to promptly identify changes in underwater species, such as migration or aggregation of large fish schools. The timely recognition of such changes offers vital information for IoUT applications, particularly in the management of marine biological resources. Furthermore, our framework can be flexibly deployed on AUVs and in-situ observation stations. Additionally, degraded underwater conditions and high acquisition costs hinder the scalability of data-driven methods. Therefore, we create a novel dense fish school detection dataset named DUFish, expertly annotated with high-quality bounding boxes. The proposed detection framework showcases exceptional performance on DUFish, outperforming state-of-the-art target detection algorithms. This has the potential to augment the capabilities of biological recognition and applications within the IoUT systems.