A Fish Target Identification and Counting Method Based on DIDSON Sonar and YOLOv5 Model-Reference-Cited by-同舟云学术

A Fish Target Identification and Counting Method Based on DIDSON Sonar and YOLOv5 Model

Published:2024-08-31 Issue:9 Volume:9 Page:346
ISSN:2410-3888
Container-title:Fishes
language:en
Short-container-title:Fishes

Author:

Shen Wei¹^ORCID,Liu Mengqi¹,Lu Quanshui¹,Yin Zhaowei¹,Zhang Jin¹²³^ORCID

Affiliation:

1. College of Oceanography and Ecological Science, Shanghai Ocean University, Shanghai 201306, China

2. Office of Asset and Laboratory Management, Shanghai Ocean University, Shanghai 201306, China

3. College of Marine Living Resource Sciences and Management, Shanghai Ocean University, Shanghai 201306, China

Abstract

In order to more accurately and quickly identify and count underwater fish targets, and to address the issues of excessive reliance on manual processes and low processing efficiency in the identification and counting of fish targets using sonar data, a method based on DIDSON and YOLOv5 for fish target identification and counting is proposed. This study is based on YOLOv5, which trains a recognition model by identifying fish targets in each frame of DIDSON images and uses the DeepSort algorithm to track and count fish targets. Field data collection was conducted at Chenhang Reservoir in Shanghai, and this method was used to process and verify the results. The accuracy of random sampling was 83.56%, and the average accuracy of survey line detection was 84.28%. Compared with the traditional method of using Echoview to process sonar data, the YOLOv5 based method replaces the step that requires manual participation, significantly reducing the time required for data processing while maintaining the same accuracy, providing faster and more effective technical support for monitoring and managing fish populations.

Publisher

MDPI AG

Link

https://www.mdpi.com/2410-3888/9/9/346/pdf

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