## 2 Related Work  
Historically, manual inspection has been the predominant approach for detecting dead chickens on broiler farms, requiring no additional tools or technology. However, its effectiveness diminishes in high-density farming environments, where the sheer scale of operations and potential for human oversight make it unsustainable [2, 17]. Previous attempts to automate the process have relied on machine learning models such as Random Forests (RFs) [4] and Support Vector Machines (SVMs) [5, 6, 21], which classify dead chickens based on manually extracted features like color, texture, or shape. [PERSON] et al. [22] used an SVM for dead chicken detection. [PERSON] et al. [23] employed RF to predict dead-on-arrival incidents of broiler chickens. [PERSON] et al. [24] proposed a dead and sick chicken detection method by fastening a foot ring on each chicken to capture the activity intensity of the chicken and using a machine learning classification method to identify the status of the chicken. Existing models often fail to generalize effectively in real-world farm environments, where challenges such as varying lighting, occlusions, and crowded spaces significantly impact their accuracy [12].  
Recent advancements in deep learning have led to significant improvements in object detection and classification in agriculture, particularly in crop monitoring and livestock behavior analysis [12]. [PERSON] et al. [15] used YOLOv4 [25] for the identification of dead chickens. [PERSON] et al. [16] used YOLOv5s for automated early mortality detection in a cage-free environment. [PERSON] et al. [14] proposed improved YOLOv7 [26] for detecting dead caged hens. Transformers, originally developed for natural language processing, have recently gained traction in computer vision tasks [9]. [PERSON] et al. [13] used Deformable DETR for the detection of dead laying hens on a commercial farm. ViTs including DeiT are increasingly being used for image classification due to their ability to capture long-range dependencies in data.  
Dynamic frame capture strategies are crucial in surveillance systems where object appearance and movement change over time. By filtering frames, these strategies reduce storage and processing complexities, improving efficiency and ensuring critical events are captured without overwhelming the system [27].  
Knowledge distillation, where a smaller, more efficient student model is trained using the knowledge learned by a larger teacher model, has been shown to reduce computational complexity while maintaining high accuracy [20]. For example, [PERSON] et al. [28] developed a multistage knowledge distillation model for lightweight and efficient plant disease detection. [PERSON] et al. [29] used knowledge distillation-based methods for the real-time semantic segmentation of surface defects in navel oranges. [PERSON] et al. [11] proposed a similar scheme for enhancing chicken health detection.  
One key challenge in deploying deep learning models for dead chicken detection is the lack of high-quality, labeled datasets. Most broiler farm datasets do not explicitly annotate dead chickens, and visually distinguishing between stationary and dead chickens is difficult due to their similarity. Creating fully annotated datasets is resource-intensive, prone to human error, and impractical in large-scale farm operations.  
To address this, we propose a two-step approach. First, stationary regions in the footage--likely representing dead chickens--are detected. Then, a deep learning classifier,leveraging knowledge distillation, is applied to confirm that the detected stationary object is a chicken, refining the classification. By employing EfficientNet-B0 as the teacher model and DeiT-Tiny as the student, the knowledge distillation process enables the student model to generalize effectively, even with limited annotated data. Combined with dynamic frame capture, this method balances real-time performance with computational efficiency, making it ideal for high-density broiler farm environments.