Slim-YOLO-PR_KD: an efficient pose-varied object detection method for underground coal mine

Abstract

Real-time object detection in underground coal mine is a crucial task in the development of AI-assisted supervision systems. Due to the complex environment of the underground coal mine, limited computing resources, and the variability of object poses, the general object detection algorithms cannot provide good performance. Hence, an improved underground pose-varied object detection method named Slim-YOLO-PR_KD has been proposed. By designing an efficient pose-varied attention module (EPA) for the backbone network, providing a receive field block (RFB) module for the neck network, and optimizing the loss function, the underground pose-varied detection model YOLO-PR is obtained, which achieved good accuracy but reduced speed. For YOLO-PR, the study improved the original module by designing RFB_SK, a lightweight C2f_GSG module, a shared parameter detection head and selectively replaced modules to slim down the whole network, resulting in a lightweight detection model Slim-YOLO-PR. By using an attention guided knowledge distillation of underground object detection method and using YOLO-PR as the teacher model, the efficient pose-varied detection model Slim-YOLO-PR_KD for coal mine underground is proposed. The experimental results show that compared with the baseline model, the proposed Slim-YOLO-PR_KD has a faster detection speed, achieving higher detection accuracy while reducing model parameters and computational complexity by 42% and 46% respectively, making it capable of performing real-time underground detection tasks. Compared with other general detection models, Slim-YOLO-PR_KD exhibits excellent performance in real-time pose-varied object detection tasks in complex environments of underground coal mines.