# Camera-Adaptive Foreign Object Detection for Coal Conveyor Belts
## 2 Related Work  
### Object Detection Models  
Many studies use deep learning techniques to detect foreign objects on coal conveyor belts. YOLOv8s-GSC [11] proposed a lightweight real-time coal gangue detection model using YOLOv8s, integrating GhostNet and a coordinate attention mechanism to enhance efficiency. [PERSON] et al. [12] designed a specialized CNN to classify coal and gangue images by incorporating dilated convolutions to enlarge the receptive field without increasing the computational burden. This enhanced the network's ability to capture multi-scale information. [PERSON] et al. [13] developed a fully convolutional U-Net-based architecture for coal and gangue segmentation, leveraging skip connections to retain spatial information during down-sampling. [PERSON] et al. [14] proposed a method for foreign object detection method for coal mine conveyor belts based on EfficientNetV2. This method improves the attention mechanism and loss function to enhance the expression ability of MBConv and Fused-MBConv for image features and the adaptability of the model to complex data distributions and task requirements. [PERSON] et al. [15] proposed coordinate attention (CA) to enhance the location information of foreign objects in the detection of foreign objects on conveyor belts, and improved the loss function to balance the detection accuracy and detection efficiency. [PERSON] et al. [16], aiming at the problems of slow speed and excessive parameters in the deep learning-based detection methods for foreign objects on conveyor belts, proposed a more lightweight detection method based on Yolov8n, which significantly reduced the number of model parameters and complexity. Although these methods have improved foreign object detection in the coal industry, it is still challenging when dealing with the variability of real-world conditions. Most models are trained in controlled environments, reducing their robustness under different camera background settings.  
In the field of object detection, OZF [17] and AutoAssign [18] improve detection performance by optimizing label matching and dynamically adjusting label allocation. CEASC [19] and AFPN [20] enhance feature quality through multi-stage fusion and global context enrichment. VarifocalNet [10] and CentripetalNet [21] introduce Intersection-over-Union (IoU)-aware loss and center keypoint mechanisms to enhance confidence and accuracy. Sparss-RCNN [22] uses sparse convolution instead of traditional convolution operation to extract the features of large-scale targets. [PERSON] et al. [23], aiming at the deficiencies of unsupervised pre-training methods in image classification, abandoned the contrastive learning for global and local images by conducting multi-level detection of the representations in the intermediate layers of the model, which improved the detection and classification performance. [PERSON] et al. [24] in response to the dim lighting environment in the underground for the task of detecting foreign objects on conveyor belts, effectively enhanced the low-light environment. Moreover, they optimized feature extraction through a lightweight dynamic group module network, and ultimately significantly improved the accuracy and real-time performance of detecting foreign objects on conveyor belts. These methods, however, also suffer from camera adaptability issues. A trained model often shows a significant performance drop when inferring images from new cameras. However, [PERSON] et al. [25] proposed a general method for generalized object detection in the underwater domain. By extracting domain-invariant features from the source domain and guiding the deep feature mining in a self-supervised learning manner, they achieved good detection results. In the proposed CAFOD model, we use data augmentation and belt area constraints to enhance camera adaptability and minimize background interference, improving the model's generalization across different cameras.