Underwater object detection and datasets: a survey

Abstract

Abstract The rapidly growing exploitation and utilization of marine resources by humans has sparked considerable interest in underwater object detection tasks. Targets captured in underwater environments differ significantly from those captured in general images owing to various factors, such as water turbidity, complex background conditions, and lighting variations. These adverse factors pose a host of challenges, such as high intensity noise, texture distortion, uneven illumination, low contrast, and limited visibility in underwater images. To address the specific difficulties encountered in underwater environments, numerous underwater object detection methods have been developed in recent years in response to these challenges. Furthermore, there has been a significant effort in constructing diverse and comprehensive underwater datasets to facilitate the development and evaluation of these methods. This paper outlines 14 traditional methods used in underwater object detection based on three aspects that rely on handmade features. Thirty-four more advanced technologies based on deep learning were presented from eight aspects. Moreover, this paper conducts a comprehensive study of seven representative datasets used in underwater object detection missions. Subsequently, the challenges encountered in current underwater object detection tasks were analyzed from five directions. Based on the findings, potential research directions are expected to promote further progress in this field and beyond.