Unlabeled Video Retrieval Method of Mining Personnel Based on MK-YOLOV4

Precise positioning and accurate identification of personnel entering, exiting, and conducting important production activities in the mining area are important foundations for achieving intelligent and safe production in the mining area. This study proposes an unlabeled video retrieval method for personnel in the mining area using MKYOLOV4 in the complex mining area production environment, which can realize multiperson target detection and reidentification of an individual's identity on unlabeled video of important gateway monitoring in the mining area. First, this study proposes the MK-YOLOV4 algorithm to achieve multiperson detection of unlabeled videos by building multiscale predictions on YOLOV4, and the K-means++ algorithm is combined to generate an anchor box that meets the characteristics of the samples, which can improve the representation learning of the convolutional neural network for small targets. Second, we propose a channel attention feature extraction network based on appearance invariance to achieve accurate reidentification of personnel in mining areas. Aiming at solving the problem of uniform work clothes for personnel in mining areas, this study proposes a weight-constrained difficult sample sampling loss function with two data enhancement strategies, where Color jitter and random erasure are combined to improve the accuracy and robustness of the identification network. Finally, according to the characteristics of the existing training dataset with few categories and single scene samples, a Miner-Market mining personnel reidentification dataset is constructed with the characteristics of the mining scenes, and the proposed method is verified on the standard dataset and Miner-Market dataset. The verification confirmed that the proposed method has high retrieval performance and recognition accuracy.