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Traditional object detection algorithms require manual feature extraction when recognizing abnormal behavior of miners queuing, resulting in long detection time and low detection precision. The object detection algorithm based on convolutional neural networks has improved detection speed and precision. But its detection performance is difficult to guarantee in scenarios of obstruction, dimness, and uneven illumination. In order to solve the above problems, an improved YOLOv5s (HPI YOLOv5s) model is proposed. It is used for miner queue detection. The HPI-YOLOv5s model improves the path aggregation network (PANet) on the basis of the YOLOv5s model. By deleting a single input edge node and adding bidirectional crossing paths, a bidirectional cross feature pyramid network (BCrFPN) is constructed for multi-scale feature fusion. Considering the low robustness of label allocation strategies with manually set thresholds, a dynamic label allocation strategy (ATSS-PLUS) is proposed based on adaptive training sample selection (ATSS) to dynamically set thresholds. It can reasonably evaluate the quality of candidate samples and dynamically set thresholds for each real object, resulting in higher detection precision and robustness. The method calculates the intersection area between the face frame and the designated queue area using the half plane intersection method. The method compares the ratio of the intersection area to the face frame area with the set threshold to determine whether the miners are queuing in an orderly manner. The experimental results show that the HPI-YOLOv5s model has an accuracy improvement of 1.9%, a weight reduction of 32%, a parameter reduction of 6.9%, and a detection speed improvement of 7.8% compared to the YOLOv5s model. Moreover, it can more accurately recognize the queuing situation of miners in obstruction, dimness, and uneven illumination mine images.