# Camera-Adaptive Foreign Object Detection for Coal Conveyor Belts
## 1 Introduction  
Coal mining operations heavily rely on conveyor belt systems to transport coal from extraction sites to storage or processing facilities, ensuring efficiency and productivity in the industry [1, 2]. However, foreign objects such as rocks, coal gauge, tools, and broken equipment parts can accumulate on the conveyor belt due to various factors, including operational mishandling or mechanical failures. These foreign objects pose significant risks, potentially causing equipment damage, operational disruptions, and severe safety hazards [3]. Therefore, accurate and efficient foreign object detection is critical to ensuring safe and smooth coal transportation [4, 5]. Recent advances in deep learning-based object detection have shown remarkable success across diverse domains, including optical remote sensing images [6], autonomous driving [7], and vehicle recognition [8, 9]. However, applying these techniques to foreign object detection on coal conveyor belts remains challenging due to the unique environmental conditions and variations in camera perspectives. One primary challenge arises from the substantial variations in camera perspectives across different working environments, as shown in Figure 1. Changes in viewpoint canalter the appearance of both foreign objects and their backgrounds, making it difficult for a model to adapt effectively to new unseen camera perspectives. Traditional deep learning models often experience significant performance degradation when applied to images from different angles. Fine-tuning the model on new camera data is a common approach, but collecting and annotating data across multiple coal mining sites is both labor-intensive and costly. Moreover, existing models are prone to false detection, especially when background elements outside the conveyor belt are mistakenly classified as foreign objects. These challenges highlight the need for a more robust detection approach that effectively addresses viewpoint variations and improves adaptability across different cameras. In response to these challenges, we propose the Camera-Adaptive Foreign Object Detection (CAFOD) model, specifically designed to enhance generalization across diverse camera perspectives. CAFOD is built on two key observations: (1) foreign objects must lie on the conveyor belt, and (2) their immediate surroundings are predominantly coal. Based on these insights, CAFOD integrates multiple strategies to achieve robust and efficient performance. First, we introduce Multi-View Data Augmentation (MVDA) to simulate a wide range of camera viewpoints during training, enabling the model to learn view-invariant features. We then incorporate Context Feature Perception (CFP) to leverage local coal-background information, reducing the likelihood of false detections in non-coal regions. Additionally, we propose a Conveyor Belt Area Loss (CBAL), which enforces explicit attention to the conveyor belt region, minimizing background interference. Finally, we integrate Varifocal Loss (VFL) [10], allowing the model to adaptively reweight challenging samples and improve the detection of small or visually similar foreign objects. These coordinated methods enable CAFOD to successfully handle viewpoint variability and achieve high detection accuracy in real coal conveyor belt environments. Through these enhancements, CAFOD can improve model generalization across different camera perspectives, effectively addressing the limitations of conventional deep learning-based object detection methods. The key contributions of this work are summarized as follows:  
* We introduce a novel approach to camera-adaptive foreign object detection that enhances generalization across varying camera perspectives without requiring extensive retraining, through viewpoint data augmentation, background feature enhancement, and belt area constraint. * We incorporate Context Feature Perception (CFP) and Conveyor Belt Area Loss (CBAL) to enhance scene understanding.