Dynamic fry counting based on multi-object tracking and one-stage detection

Abstract

Fry counting is an essential task in aquaculture that provides valuable information for feedstuff feeding, culture density adjustment, and fish economic benefit estimation. However, most current computer vision-based fry counting methods are designed for statically counting, with a fixed number of fry in a container. As a result, these methods are not suitable for dynamic scenarios, such as flowing water, where the effects of factors like water velocity and uneven lighting can cause errors in counting. To address this issue and improve accuracy, we propose a dynamic fry counting method capable of dealing with large-number fry counting. Specifically, we regard fry counting as a Multiple Object Tracking (MOT) problem based on Tracking-By-Detection (TBD) framework. Firstly, a dataset of streaming fry is constructed for training the detection model, and videos with varying numbers of fry are manually counted for counting tests. Secondly, due to the highly similar appearance of fry, Simple Online and Realtime Tracking (SORT) is combined with You Only Look Once version 5 Nano (YOLOv5-Nano) to perform fry tracking. To achieve stable tracking of fry movement, the SORT algorithm is improved based on multi-matching and trajectory recovery, reaching 82.6 % multi-object tracking accuracy. Finally, fry counting is realized based on the intersection of a counting line and moving trajectories. The results indicate that the proposed counting method achieves 96.4 % accuracy and can be implemented on CPU at 5–10 frames per second (fps) and GPU at 15–35 fps. In conclusion, the proposed method automatically counts the number of fry in videos with higher speed and accuracy, facilitating automated fishery farming.