Research on duck egg recognition algorithm based on improved YOLOv4

Abstract

ABSTRACT 1. The following study addressed the problem of small duck eggs as challenging to detect and identify for pick up in complex free-range duck farm environments. It introduces improvements to the YOLOv4 convolutional neural network target detection algorithm, based on the working conditions of egg-picking robots. 2. Specifically, one scale of anchor boxes was removed from the prediction network, and a duck egg labelling dataset was established to make the improved algorithm YOLOv4-ours better match the working state of egg-picking robots and enhance detection performance. 3. Through multiple comparative experiments, the YOLOv4-ours object detection algorithm exhibited superior overall performance, achieving a precision of 98.85%, recall of 96.67%, and an average precision of 98.60% and F1 score increased to 97%. Compared to the original YOLOv4 model, these improvements represented increases of 1.89%, 3.41%, 1.32%, and 1.04%, respectively. Furthermore, detection time was reduced from 0.26 seconds per image to 0.20 seconds. 4. The enhanced model accurately detected duck eggs in free-range duck housing, effectively meeting the real-time egg identification and picking requirements.