Review of Underwater Image Object Detection Based on Deep Learning

Underwater image object detection is one of the core technologies of underwater intelligent exploration, which is widely used in industrial and military fields. The breakthrough of deep learning related technologies has brought new opportunities for the development of underwater image object detection, but the current reviews are relatively old and lack a certain degree of systematicness and comprehensiveness. In this paper, the research of underwater visible and sonar image detection based on deep learning is summarized and analyzed in detail. Firstly, the general object detection algorithm framework based on deep learning is sorted out, including six elements: backbone, neck, head, training algorithm, inference strategy, and evaluation criteria, and the problems of each element and the latest research work are systematically summarized; Then, the latest progresses of underwater visible image object detection are investigated and summarized from three aspects: data set, model design, and training method; Meanwhile, the works related to underwater sonar image detection are summarized and analyzed, including forward-looking sonar, side-scanning sonar and synthetic aperture sonar. Finally, the research trend of underwater image object detection is discussed based on the latest research on deep learning.