Lightweight Coal Flow Foreign Object Detection Algorithm

In response to the challenges of foreign object detection and high real-time requirements in complex coal mine monitoring scenarios, a lightweight coal flow foreign object detection algorithm, FAI_YOLO, was developed based on YOLOv8s, which incorporates several innovations to address these challenges. Initially, Fastnet is employed as the backbone feature extraction network to minimize redundant computation and memory access, thereby accelerating inference speed. Additionally, AKConv replaces the traditional convolution operation in C2f module, and the loss function ImpIOU is refined to enhance regression performance. Experimental results suggest that this algorithm markedly improves the speed of foreign object detection in coal flow compared to the original YOLOv8s model, decreasing frame reasoning time by 33.8%. While the map@0.5 metric experiences a minor decrease of 0.03%, the algorithm continues to provide high detection accuracy and effectively manages the demands of real-time and accurate foreign object detection in complex coal mine monitoring scenarios.