Affiliations: [a] School of Information Engineering, Dalian Ocean University, Dalian, China
| [b] School of Software, Dalian Jiaotong University, Dalian, China
Correspondence:
[*]
Corresponding author. Kaiyan Zhu, School of information engineering, Dalian Ocean University, Dalian 116023, China. E-mail: [email protected].
Abstract: 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.
Keywords: Marine fishery, fishing supervision, real time fish detection, YOLO-V3-Tiny, MobileNet
