Using YOLOv5-DSE for Egg Counting in Conventional Scale Layer Farms

Abstract

Given that common egg counting methods in conventional layer farms are inefficient and costly, there is a growing demand for cost-effective solutions with high counting accuracy, expandable functionality, and flexibility that can be easily shared between different coops. However, accurate real-time egg counting faces challenges due to small size, density variation, and egg similarity, exacerbated by dynamic poses. Moreover, current animal industry methods emphasize single-image counting, limiting suitability for video-based counting due to a lack of frame-to-frame target association. The you only look once version 5-DeepSORT-spatial encoding (YOLO v5-DSE) algorithm is proposed as a solution for efficient and reliable egg counting to tackle these issues. The algorithm contains the following three main modules: 1) the egg detector utilizes the improved YOLOv5 to locate eggs in video frames automatically, 2) the DeepSORT-based tracking module is employed to continuously track each egg's position between frames, preventing the detector from losing egg localization, and 3) the spatial encoding (SE) module is designed to count eggs. Extensive experiments are conducted on 4808 eggs on a commercial farm. Our proposed egg-counting approach achieves a counting accuracy of 99.52% and a speed of 22.57 fps, surpassing not only the DeepSORT-SE and ByteTrack-SE versions of eight advanced YOLO-series object detectors (YOLOX, and YOLOv6-v9) but also other egg-counting methods. The proposed YOLOv5-DSE provides real-time and reliable egg counting for commercial layer farms. This approach could be further expanded to the egg conveyor to locate cages for low-lying hens and help companies cull more efficiently.