Underwater Robot Target Detection Based On Improved YOLOv5 Network

Underwater target detection is pivotal in underwater robotics. While models based on deep learning excel in terrestrial applications, they often falter underwater due to challenges like intricate backgrounds, inferior image quality, minute and clustered targets, and the constrained computational resources of sub-aquatic devices. Addressing these issues, our research presents a groundbreaking detection network, meticulously engineered to optimize both accuracy and processing speed. Our approach employs the CLAHE algorithm for noise reduction and image enhancement, significantly improving underwater visual clarity. The network, an innovative adaptation of YOLOv5s, integrates a cutting-edge C3GD module in its core. This module, inspired by the Ghost paradigm and depth-wise separable convolutions, streamlines the model by minimizing parameters and overall bulk, without compromising performance. Further refinement is found in the network's 'neck' through a sophisticated attention-fueled Res-CM module, bolstering detection precision, especially for smaller, challenging targets. Rigorous testing on the URPC2021 dataset confirms the superiority of our model, eclipsing standard YOLOv5s and comparable models in underwater detection efficacy. Notably, it records a performance leap of approximately 3.7 ^% , accompanied by significant optimization in model parameters and size, marking a considerable advancement in this domain.