Automated Dead Chicken Detection in Poultry Farms Using Knowledge Distillation and Vision Transformers  
[PERSON]  
1 Division of Computer Science and Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea; [EMAIL_ADDRESS] (R.K.); [EMAIL_ADDRESS] (W.W) 12 Department of Computer Science and Applications, Tribhuvan University, Mechi Multiple Campus, Bhadrapur 57200, Nepal 2  
[PERSON]  
1 Division of Computer Science and Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea; [EMAIL_ADDRESS] (R.K.); [EMAIL_ADDRESS] (W.W) 1  
[PERSON]  
1 Division of Computer Science and Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea; [EMAIL_ADDRESS] (R.K.); [EMAIL_ADDRESS] (W.W) 1  
Footnote 1: email: {  
Existing automated detection systems typically rely on classical computer vision techniques or traditional machine learning models such as Random Forests (RFs) [4] and Support Vector Machines (SVMs) [5; 6]. These methods classify chickens based on manually extracted features like color, texture, or shape. However, in real-world farm environments, these models struggle to maintain accuracy due to dynamic challenges such as fluctuating lighting, occlusions, cluttered backgrounds, and the continuous growth of chickens [7]. Additionally, distinguishing between dead and stationary chickens becomes difficult, especially in high-density settings where visual similarities exist. These conventional methods struggle to maintain consistent performance, particularly when faced with the diverse and unpredictable conditions of modern broiler farms. Advancements in deep learning have opened new possibilities for automating tasks in agriculture. Deep learning models, particularly convolutional neural networks (CNNs) [8] and vision transformers (ViTs) [9], have demonstrated significant improvements in object detection and classification across various domains, including crop monitoring and livestock analysis [3; 7; 10; 11; 12; 13; 14; 15; 16; 17]. In this context, knowledge distillation has emerged as an effective technique to train smaller, efficient models (students) by transferring knowledge from larger, more accurate models (teachers). This process helps reduce the computational complexity of the student model without compromising accuracy, making it ideal for applications in real-time, resource-constrained environments like broiler farms [18]. A major challenge in training deep learning models for dead chicken detection is the absence of explicit annotations for dead chickens, making it difficult to create accurately labeled datasets. Manually labeling dead and stationary chickens is labor-intensive and prone to mistakes due to their visual similarities. To address this, our approach first detects stationary regions likely to represent dead chickens and then applies a deep learning classifier to confirm whether the detected object is a chicken. This two-stage method leverages EfficientNet [19] as the teacher model and a distilled vision transformer (DeiT) [20] as the student, helping to overcome the lack of precise annotations while maintaining high performance in real-world farm conditions. Our proposed approach addresses the limitations of existing methods by integrating stationary object detection with a knowledge-distilled classifier, achieving superior accuracy compared to traditional models like Random Forest. The two-stage method effectively distinguishes dead from inactive chickens, ensuring robustness in high-density settings. Knowledge distillation enables real-time applicability by creating a lightweight yet powerful student model, while the dynamic frame capture strategy improves processing efficiency, making the system scalable for large-scale broiler farms. This paper presents a novel automated system for detecting dead chickens using CCTV footage, combining deep learning techniques and knowledge distillation to enhance detection accuracy and efficiency.