Deep neural network-based real time fish detection method in the scene of marine fishing supervision

• Published in: Journal of intelligent & fuzzy systems 2021-01, Vol.41 (3), p.4527-4532
• Main Authors: Li, Junpeng, Zhu, Kaiyan, Wang, Fei, Jiang, Fengjiao
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Datasets; Feature extraction; Fisheries; Fishing; Fishing vessels; Marine ecology; Monitoring; Overfishing; Real time; Supervision; Vessels
• ISSN: 1064-1246; 1875-8967
• DOI: 10.3233/JIFS-189713

Overfishing of marine fishery is a serious threat to fishery ecological security. Fishing supervision is one of the main ways to maintain marine fishery ecology. In order to improve the intelligence of fishing supervision system, a real time fish detection method based on YOLO-V3-Tiny-MobileNet was proposed. Aiming at the problems of shallow network layers and insufficient feature extraction ability in YOLO-V3-Tiny network, the proposed network takes YOLO-V3-Tiny as baseline and combines it with MobileNet. The proposed network is pre-trained by VOC2012 dataset, and then retrained and tested on Kaggle_ NCFM (The Nature Conservancy Fisheries Monitoring) dataset. The experimental results show that the proposed method has superior performance in parameters number, mean average precision and detection performance, compared with other methods. Compared with the monitoring method of fishing vessel detection on shore supervision, the real time monitoring method can give timely warning to the fishing vessel operators, which is more conducive to fishery ecological protection.