Underwater Small Object Detection: A Survey

Abstract

Abstract Underwater small object detection is one of the emerging core technologies at the intersection of computer vision and underwater imaging detection, which aims to achieve efficient and accurate detection and identification of faint, small targets in underwater environments, and has been widely applied in fields of aquatic organism detection, mineral resource exploration and other fields. Among them, underwater object detection methods based on optical images, with their high resolution and good flexibility, have demonstrated significant advantages in underwater biological detection, recognition and other scenarios. However, there is currently a relative lack of comprehensive reviews on existing research regarding underwater small object detection. To address this gap, this paper presents a systematic review of small object detection in underwater optical images. Firstly, at the level of problems and data, the specific problems faced by underwater small detection are comprehensively sorted out and classified into two categories: the inherent characteristics of underwater small objects, and the degradation characteristics of underwater images. Then 16 existing typical datasets are sorted out and analyzed in detail. Subsequently, at the methodological level, existing underwater small object detection algorithms are systematically summarized from the perspectives of traditional machine learning and deep learning, and an in-depth analysis of improved deep learning-based strategies is conducted by covering five aspects: data augmentation and pre-processing, multi-scale feature fusion, attention mechanism introduction, loss function optimization, and lightweight design. Finally, the challenges faced by underwater small object detection at present are pointed out, and the potential directions for future development are explored, aiming to provide valuable references for subsequent related research.