Recent Progress in Ocean Intelligent Perception and Image Processing and the Impacts of Nonlinear Noise - Huayu Liu; Ying Li; Tao Qian; Ye Tang (Mathematics)
Deep learning network models are crucial in processing images acquired from optical, laser, and acoustic sensors in ocean intelligent perception and target detection. This work comprehensively reviews ocean intelligent perception and image processing technology, including ocean intelligent perception devices and image acquisition, image recognition and detection models, adaptive image processing processes, and coping methods for nonlinear noise interference. As the core tasks of ocean image processing, image recognition and detection network models are the research focus of this article. The focus is on the development of deep-learning network models for ocean image recognition and detection, such as SSD, R-CNN series, and YOLO series. The detailed analysis of the mathematical structure of the YOLO model and the differences between various versions, which determine the detection accuracy and inference speed, provides a deeper understanding. It also reviewed adaptive image processing processes and their critical support for ocean image recognition and detection, such as image annotation, feature enhancement, and image segmentation. Research and practical applications show that nonlinear noise significantly affects underwater image processing. When combined with image enhancement, data augmentation, and transfer learning methods, deep learning algorithms can be applied to effectively address the challenges of underwater image degradation and nonlinear noise interference. This work offers a unique perspective, highlighting the mathematical structure of the network model for ocean intelligent perception and image processing. It also discusses the benefits of DL-based denoising methods in signal–noise separation and noise suppression. With this unique perspective, this work is expected to inspire and motivate more valuable research in related fields.