To address the issues of existing conveyor belt foreign object detection models in coal mines, which perform poorly in low-light environments, miss elongated and small foreign objects, and have a large model size that hinders deployment on edge devices, this paper proposes a low-light coal mine conveyor belt foreign object detection algorithm based on an improved YOLOv8. First, image enhancement techniques are applied to preprocess low-light images to enhance the effective feature information of foreign objects on the coal mine conveyor belt. Next, dynamic snake convolution is introduced into the model′s backbone network to dynamically adjust the convolution kernel shape, improving the model′s focus on elongated foreign objects. Additionally, a slim-neck design paradigm is used to modify the neck network, significantly reducing the model′s parameters while maintaining learning capability. Finally, the Inner-CIoU loss function is employed to replace the CIoU loss function, accelerating the model′s convergence and improving its detection performance for elongated and small foreign objects. Experimental results show that, compared to the baseline model, the improved algorithm increases the average detection accuracy by 1.6%, reduces the model size by 29.7%, and improves the detection speed (FPS) by 59%, validating its effectiveness. In comparison with other advanced models, it is proved that the proposed algorithm still has strong recognition ability in complex environment.