preprints.org > engineering > marine engineering > doi: 10.20944/preprints202309.1686.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 25 September 2023 / Approved: 25 September 2023 / Online: 25 September 2023 (11:30:23 CEST)

Sea cucumber detection represents a significate step in underwater environmental perception, which is an indispensable part of the intelligent subsea fishing system. However, various complex factors such as water turbidity declines the clarity of underwater images, presenting a challenge to vision-based underwater target detection. Therefore, accurate, real-time and lightweight detection models are required. First of all, the development of subsea target detection is summarized in this presented work. Besides, since target detection methods based on deep learning such as YOLOv5 and DETR, which are respectively examples of one-stage and anchor-free deep leaning methods, have been increasingly applied in underwater detection scenarios. Based on the analysis of state-of-the-art underwater sea cucumber detection approaches and aiming to provide a reference for practical subsea identification, the sea cucumber detection based on the YOLOv5 and DETR are investigated and compared in detail. For each approach, the detection experiment is carried out on the derived dataset which contains a wide variety of sea cucumber sample images. The compared experiments demonstrate that the overall outperformance of YOLOv5 in terms of low computing consumption and high precision, particularly in detection of small and dense features. Nevertheless, the DETR exhibits rapid development and holds promising prospects in underwater object detection applications, owing to its relatively simple architecture and ingenious attention mechanism.

underwater target detection and recognition; YOLOv5; DETR; sea cucumber fishing

Engineering, Marine Engineering

* All users must log in before leaving a comment