###### Abstract
In more severe cases, it could lead to coal accumulation or blockage on the conveyor belt, potentially causing material stack collapses, posing risks to worker safety, and resulting in equipment damage. Therefore, the rapid identification and monitoring of non-coal foreign objects on conveyor belts are imperative. As condition monitoring technology advances, the monitoring methods for belt damage in conveyor belts have gradually evolved from manual inspection to traditional image recognition algorithms and deep learning-based object detection methods. Manual inspection methods are inefficient and costly. In contrast to traditional image recognition algorithms, deep learning-based detection methods do not require manual feature extractor design and possess stronger feature extraction capabilities, meeting the demands of efficient and precise data processing in the age of big data [12]. Currently, deep learning object detection methods can be categorized into single-stage and two-stage algorithms. Classic two-stage algorithms include Region-based Convolutional Neural Network (R-CNN) [13], Fast Region-based Convolutional Neural Network (Fast R-CNN) [14], and Faster Region-based Convolutional Neural Network (Faster R-CNN) [15]. However, two-stage methods require generating candidate boxes from input images before feature extraction and detection, resulting in a relatively slow detection speed, which fails to meet the high demands of real-time performance and detection speed in coal mining applications. Classic single-stage algorithms encompass the You Only Look Once (YOLO) series and the Single Shot MultiBox Detector (SSD) algorithm [16]. Unlike the two-stage methods, single-stage algorithms directly extract category, coordinate, and other feature information while generating candidate boxes in a single step to obtain detection results. The YOLO series algorithms, known for their accuracy and fast speed, are widely used in coal mine foreign object detection due to their applicability to embedded mobile platforms. [PERSON] et al. [17] proposed an improved YOLOv3-based method for foreign object detection in belt conveyor systems. This approach employs the Focal Loss function as its loss function and fine-tunes the optimal hyperparameters, including weight parameter \(\alpha\) and focus parameter \(\gamma\), to address the sample imbalance problem. The highest recognition accuracy on the proposed non-coal foreign object dataset was 94.0%. [PERSON] et al. [18] proposed an improved YOLOv4-based approach using depthwise separable convolutions to construct a series of lightweight networks, achieving a detection accuracy of 93.7% on the proposed dataset. [PERSON] et al. [19] proposed a coal gangue object detection method for belt conveyors based on YOLOv5s-SDE. By adding a Squeeze-and-Excitation (SE) attention mechanism to the backbone network and optimizing the loss function, they improved the model's convergence speed and prediction accuracy. The results showed a maximum detection accuracy of 92.5% and a recognition speed of 30 frames per second. [PERSON] et al. [20] introduced a foreign object recognition approach for coal mine belt conveyors, which is based on an improved version of YOLOv7. By introducing deep separable convolutions instead of ordinary convolutions in network models, the foreign object recognition speed was improved, with a recognition accuracy of 92.8% and a recognition speed of 25.64 frames/s. Although deep learning-based foreign object detection methods have become mainstream in current detection, the video surveillance systems serving the belt conveyor field rely on network cameras or inspection robots for data acquisition, which is then uploaded to central servers for centralized processing. This \"cloud computing\" processing approach exhibits relatively high network latency, which has a substantial impact on the real-time nature and accuracy of system alerts.