The rapid development of convolutional neural networks has significant implications for automated underwater fishing operations. Among these, object detection algorithms based on underwater robots have become a hot topic in both academic and applied research. Due to the complexity of underwater imaging environments, many studies have employed large network structures to enhance the model’s detection accuracy. However, such models contain many parameters and consume substantial memory, making them less suitable for small devices with limited memory and computing capabilities. To address these issues, a YOLOv6-based lightweight underwater object detection model, YOLOv6-ESG, is proposed to detect seafood, such as echinus, holothurian, starfish, and scallop. First, a more lightweight backbone network is designed by rebuilding the EfficientNetv2 with a lightweight ODConv module to reduce the number of parameters and floating-point operations. Then, this study improves the neck layer using lightweight GSConv and VoVGSCSP modules to enhance the network’s ability to detect small objects. Meanwhile, to improve the detection accuracy of small underwater objects with poor image quality and low resolution, the SPD-Conv module is also integrated into the two parts of the model. Finally, the Adan optimizer is utilized to speed up model convergence and further improve detection accuracy. To address the issue of interference objects in the URPC2022 dataset, data cleaning has been conducted, followed by experiments on the cleaned dataset. The proposed model achieves 86.6% mAP while the detection speed (batch size = 1) reaches 50.66 FPS. Compared to YOLOv6, the proposed model not only maintains almost the same level of detection accuracy but also achieves faster detection speed. Moreover, the number of parameters and floating-point operations reaches the minimum levels, with reductions of 75.44% and 79.64%, respectively. These results indicate the feasibility of the proposed model in the application of underwater detection tasks.

Visual prostheses, used to assist in restoring functional vision to the visually impaired, convert captured external images into corresponding electrical stimulation patterns that are stimulated by implanted microelectrodes to induce phosphenes and eventually visual perception. Detecting and providing useful visual information to the prosthesis wearer under limited artificial vision has been an important concern in the field of visual prosthesis. Along with the development of prosthetic device design and stimulus encoding methods, researchers have explored the possibility of the application of computer vision by simulating visual perception under prosthetic vision. Effective image processing in computer vision is performed to optimize artificial visual information and improve the ability to restore various important visual functions in implant recipients, allowing them to better achieve their daily demands. This paper first reviews the recent clinical implantation of different types of visual prostheses, summarizes the artificial visual perception of implant recipients, and especially focuses on its irregularities, such as dropout and distorted phosphenes. Then, the important aspects of computer vision in the optimization of visual information processing are reviewed, and the possibilities and shortcomings of these solutions are discussed. Ultimately, the development direction and emphasis issues for improving the performance of visual prosthesis devices are summarized.