A real-time detector of chicken healthy status based on modified YOLO

In modern times, the development of an intelligent system that can automatically detect and recognize poultry diseases is vital for efficient poultry farming and for reducing human workloads. This paper presents a real-time detector that can analyze frames captured by monitoring cameras and simultaneously detect chickens and identify their healthy statuses. To overcome the challenge of chickens appearing small and having variant scales in monitoring camera frames, we integrate a scale-aware receptive field enhancement module into the YOLOv5 algorithm to enhance the receptive filed of chicken in the frames thus improving detection accuracy. In addition, we utilize a slide weighting loss function to calculate the classification loss. This helps the network to concentrate on classifying hard classified samples, leading to an improved ability to recognize the healthy statuses of chickens with greater precision. Experimental results demonstrate the proposed detector outperforms the original YOLOv5 and other one-stage object detectors, thus meeting the requirements for automated poultry health monitoring.