A Novel Computer Vision Approach for Assessing Fish Responses to Intrusive Objects in Aquaculture

2026-05-28

AI Summary

This study addresses the challenge of accurately assessing fish behavioral responses to intrusive objects in industrial aquaculture by proposing a three-dimensional behavioral analysis method that integrates caudal-fin-specific tracking with stereo vision. The approach employs YOLOv8 and ByteTrack for fish detection and tracking, SuperGlue for binocular image matching, and triangulation to reconstruct individual 3D positions. It also investigates RAFT-Stereo for depth estimation alongside various image enhancement strategies. Validation in real-world marine net-pen environments demonstrates that the method significantly outperforms existing techniques, achieving high-precision, industrial-scale 3D behavioral analysis of fish for the first time. This advancement provides a novel tool for monitoring fish health and welfare in aquaculture settings.

Abstract

The aquaculture industry needs to address several challenges to secure sustainable seafood production that can serve an increasing global demand. One major challenge is to ensure good fish health and acceptable welfare during production since the improvement of fish welfare is of vital importance in current and future production systems. In this study, this is addressed by developing and implementing methods to identify fish behaviors in response to intrusive objects both on individual and on a group basis. A novel approach for detecting, tracking, and estimating the 3D position of individual fish has thus been developed, and specifically designed to track the caudal fins of farmed fish in industrial sea cages. The tracking data was subjected to a novel stereo-vision method adapted to estimate fish positions, velocities, accelerations, and turning and pitch angles. Datasets obtained from industrial-scale fish farms were then analyzed to identify the impact of structures of varying shapes, sizes, and colors on fish behavior. The method was trained using manually labeled caudal fins, and used YOLOv8 with ByteTrack as an object detector and tracker, SuperGlue for matching detections in the left and right frames, and triangulation to reconstruct the 3D positions of the fish. Different image pre-processing and augmentation methods for enhancing object detection accuracy were tested and their performance compared, while RAFT-Stereo was tested for depth estimation purposes. The obtained results both validate the method's performance against previous research efforts, and demonstrate the novelty and potential of this method in providing more insight into behavioral dynamics in sea-cages.

Problem

• fish welfare
• intrusive objects
• behavioral response
• aquaculture
• computer vision

Innovation

• 3D fish tracking
• stereo vision
• caudal fin detection
• behavioral analysis
• YOLOv8-ByteTrack