LAUSR

Fish are a critical component of marine biology, therefore, the accurate identification and counting of fish is essential for the objective monitoring and assessment of marine biological resources. High frequency Adaptive Resolution Imaging Sonar (ARIS) is widely used for underwater object detection and imaging, and quickly obtains near video rapidly of free-swimming fish in high turbidity water environments. However, processing the massive data output using imaging sonars remains a major challenge. Here, we developed an automatic image processing program that fuses K-Nearest Neighbor background subtraction with DeepSort target tracking to automatically track and count fish. The automatic program was evaluated using four test datasets with different target sizes and observation ranges and differently deployed sonars. According to the results, the approach successfully counted free swimming fish targets with an accuracy index of 73%, and a completeness index of 70%. Under appropriate conditions, this approach could replace time-consuming semi-automatic approaches and improve the efficiency of imaging sonar data processing, while providing technical support for future real-time data processing. This article is protected by copyright. All rights reserved.