Dynamic identification and automatic counting of the number of passing fish species based on the improved DeepSORT algorithm

Frontiers in Environmental Science

Environmental Science

Jianyuan Li¹  Zuobin Yang²  Bilang Wu³  Chunna Liu³  Furen Jiang³ 

[1] China Institute of Water Resources and Hydropower Research, Beijing, China;College of Hydraulic and Environment Engineering, China Three Gorges University, Yichang, China;Huaneng Tibet Yarlu Zangbo River Hydropower Development Investment Co., Ltd., Lasa, China;State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, Beijing, China;China Institute of Water Resources and Hydropower Research, Beijing, China;

关键词: DeepSORT;    YOLOv5;    re-identification (ReID);    dynamic identification of passing fish species;    automatic counting;   

DOI  :  10.3389/fenvs.2023.1059217

 received in 2022-10-01, accepted in 2023-03-06,  发布年份 2023

来源: Frontiers

【 摘 要 】

In this paper, based on the improved DeepSORT algorithm, four target species of passing fish (Schizothorax o’connori, Schizothorax waltoni, Oxygymnocypris stewartii and Schizopygopsis younghusbandi) from a fishway project in the middle reaches of the Y River were used to achieve dynamic identification and automatic counting of passing fish species using fishways monitoring video. This method used the YOLOv5 model as the target detection model. In view of the large deformation by fish body twisting, the network structure of the re-identification (ReID) model was deepened to strengthen the feature extraction ability of the model. It was proposed to identify and track fish that cross the line by setting a virtual baseline to achieve the dynamic identification of fish species passing and the automatic counting of upward and downward quantities. The results showed that 1) among the five models, YOLOv5n, YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x, the highest value of mean average precision (mAP) was 92.8% achieved by the YOLOv5x model. Specifically, recognition accuracies of 96.95%, 94.95%, 88.79%, and 91.93% were recorded for Schizothorax o’connori, S. waltoni, S. younghusbandi and O. stewartii, respectively. 2) The error rate of the improved ReID model was 20.3%, which was 20% lower than that before the improvement, making it easier for the model to obtain target features. 3) The average accuracy of the improved DeepSORT algorithm for counting four target fishes was 75.5%, among which the accuracy of Schizothorax o’connori, S. waltoni, S. younghusbandi and O. stewartii were 83.6%, 71.1%, 68.1%, and 79.3%, respectively. Meanwhile, the running speed was 44.6 fps, which met the real-time monitoring. This method is the first to implement intelligent identification of the target passing fish in fishways projects, which can accumulate long series monitoring data for fishways operation and management and provide a technical solution and reference for the work related to the realization of intelligent and informative passing fish monitoring.

【 授权许可】

Unknown   
Copyright © 2023 Wu, Liu, Jiang, Li and Yang.