Detection Method of Fry Feeding Status Based on YOLO Lightweight Network by Shallow Underwater Images

Abstract

Pellet feed is widely used in fry feeding, which cannot sink to the bottom in a short time, so most fries eat in shallow underwater areas. Aiming at the characteristics of fry feeding, we present herein a nondestructive and rapid detection method based on a shallow underwater imaging system and deep learning framework to obtain fry feeding status. Towards this end, images of fry feeding in shallow underwater areas and floating uneaten pellets were captured, following which they were processed to reduce noise and enhance data information. Two characteristics were defined to reflect fry feeding behavior, and a YOLOv4-Tiny-ECA network was used to detect them. The experimental results indicate that the network works well, with a detection speed of 108FPS and a model size of 22.7 MB. Compared with other outstanding detection networks, the YOLOv4-Tiny-ECA network is better, faster, and has stronger robustness in conditions of sunny, cloudy, and bubbles. It indicates that the proposed method can provide technical support for intelligent feeding in factory fry breeding with natural light.