Foreign object detection on coal mine conveyor belts is crucial for ensuring operational safety and efficiency. However, applying deep learning to this task is challenging due to variations in camera perspectives, which alter the appearance of foreign objects and their surrounding environment, thereby hindering model generalization. Despite these viewpoint changes, certain core characteristics of foreign objects remain consistent. Specifically, (1) foreign objects must be located on the conveyor belt, and (2) their surroundings are predominantly coal, rather than other objects. To leverage these stable features, we propose the Camera-Adaptive Foreign Object Detection (CAFOD) model, designed to improve cross-camera generalization. CAFOD incorporates three main strategies: (1) Multi-View Data Augmentation (MVDA) simulates viewpoint variations during training, enabling the model to learn robust, viewpoint-invariant features; (2) Context Feature Perception (CFP) integrates local coal background information to reduce false detections outside the conveyor belt; and (3) Conveyor Belt Area Loss (CBAL) enforces explicit attention to the conveyor belt region, minimizing background interference. We evaluate CAFOD on a dataset collected from real coal mines using three distinct cameras. Experimental results demonstrate that CAFOD outperforms State-of-the-Art object detection methods, achieving superior accuracy and robustness across varying camera perspectives.