A lightweight model based on knowledge distillation for free-range chickens detection in complex commercial farming environments

Side-view imaging for monitoring free-range chickens on edge devices faces significant challenges due to complex backgrounds, occlusions, and limited computational resources, which particularly affect the performance of lightweight models in terms of their representational capacity and generalization ability. To address these limitations, this study proposes a Lightweight Free-range Chickens Detection Model based on YOLOv8n and knowledge distillation (LCD-YOLOv8n-KD), establishing an optimal balance between detection performance and model efficiency. The YOLOv8n architecture is enhanced by incorporating DualConv, CCFF, PCC2f, and SAHead modules to create LCD-YOLOv8n, significantly reducing model parameters and computational complexity. Further improvement is achieved through knowledge distillation, where a pre-trained large-scale model developed by our team as the teacher network, and LCD-YOLOv8n functioned as the student network, resulting in the LCD-YOLOv8n-KD model. Experimental validation is conducted using a comprehensive dataset comprising 6000 images with 162,864 labeled chicken targets, collected from various side-view angles in commercial farming environments. LCD-YOLOv8n-KD achieves AP 50 values of 95.9 %, 90.2 %, 82.7 %, and 69.3 % on the test set and three independent test sets, respectively. Compared to the original YOLOv8n, the proposed model demonstrates a 16.13 % improvement in AP 50 while reducing parameters by 47.84 % and GFLOPs by 41.46 %. The proposed model outperforms other state-of-the-art lightweight models in terms of detection efficiency, accuracy, and generalization capability, demonstrating strong potential for practical deployment in real-world free-range chicken farming environments. • Proposed a lightweight model balancing accuracy and size for detecting free-range chickens. • Established chicken side-view datasets in complex commercial farming environments. • Optimized YOLOv8n with four lightweight modules reducing parameters and complexity. • Applied knowledge distillation to improve generalization in three independent tests. • The proposed LCD-YOLOv8n-KD outperforms other lightweight models.