Analysis of recent techniques in marine object detection: a review

Marine Object Detection, leveraging computer vision, plays a vital role in detecting objects in marine environments ranging from marine organisms to marine surveillance. However, marine environment presents unique set of challenges that makes the detection process tedious. Marine environment often has low visibility due to color cast, haziness and intricate background. Moreover, marine target objects appear at varying scales and occluded by the surrounding objects. For comprehensive understanding of this domain, we have analyzed the significant studies from the year (2012-2024) in this work. Prisma model is utilized to identify the crucial 111 studies for this review. The review organizes marine object detection techniques into conventional and advance learning approach through the proposed hierarchical tree-like structure. The organized research has been tabulated, offering an in-depth summary of significant work and highlighting the strengths and weakness of the identified studies for the conventional and advance learning approach. Furthermore, a brief examination of 31 benchmark datasets categorized according to the different application domains (marine species, ship and debris) has been conducted and their key attributes are tabulated. In addition, statistical analysis has been performed to understand the growth and future directions in marine object detection domain.