A Computer Vision Based Behavioral Study and Fish Counting in a Controlled Environment

Abstract

Aquaculture is a fast-growing food-production sector that accounts for almost 50% of the world's fish used for consumption. Aquaculture refers to the cultivation of fish in cages. The fish in the cage must be fed at regular intervals, for which the number of fish helps in estimating the amount of feed to be put in the cage. The behavior of fish in a caged environment reflects their health. In the absence of an ambient atmosphere, fish are stressed, which results in frantic movement. The frantic behavior of fish can be identified using recent advancements in image and video processing. In this study, we have focused on frantic behavior detection, fish detection, and counting. For this, a RAS with Tilapia fish has been setup, and the videos of the fish are captured. The detection and counting have been achieved by using the YOLOv5 model. The model has resulted in a precision, Recall and F1-measure of 81%. The results are compared with the ground truth, which indicates that the model has been successful in counting the fish. The frantic movement of the fish has been detected by developing an optical flow model. The results are encouraging and can be used for frantic behavior detection.