Automated Chicken Counting Using YOLO-v5x Algorithm

Abstract

Chicken counting is an essential task in large-scale farming management. Due to dense distribution, uneven illumination, and partial occlusion, accurate chicken counting remains challenging. In this paper, an automated chicken counting algorithm based on You Only Look Once (YOLO) v5x model is implemented. The intersection over union (IoU) threshold is set by analyzing the width and height of the ground truth (GT) boxes of the training images. Three objective-oriented data enhancements, i.e., Mosaic, horizontal flipping combined with lightness changing, and test time augmentation (TTA), are applied to diversify the training data. To validate the efficiency of our proposed method, extensive experiments are conducted on a well-annotated dataset collected from a real farm with 1,100 images and 170,906 chickens in total. Our implementation achieves the average_accuracy of 95.87% and inference speed of 23 ms per image, even if chickens are partially occluded in extremely uneven illumination perspectives.