Abstract: The intelligent recognition technology for Coal mine overhead passenger devices(Cmopd) plays a crucial role in achieving automated inspection, real-time monitoring, and warning tasks for cmopd, thereby promoting the intelligent development of coal mines. However, there are several challenges that need to be addressed, such as the limited number of samples in the cmopd dataset, poor lighting conditions in underground images, overlapping and occlusion between operating cmopd, varying sitting postures of miners, difficulty in detecting small cmopd targets, complex model deployment, and low efficiency of traditional recognition methods for cmopd with different passenger-carrying statuses.To overcome these challenges, a cmopd dataset was created from various coal mines in Guizhou province. The passenger-carrying status of cmopd was classified into two categories: cmopd with passengers (HC_miner) and cmopd without passengers (HC_nominer). The YOLOv8n single-stage object detection algorithm was used as the baseline model, and a coal mine cmopd intelligent recognition algorithm based on multi-scale feature fusion was proposed.In the image preprocessing stage, adaptive histogram equalization was employed to enhance image quality, and random rectangle masking was applied to simulate real scenarios where cmopd is occluded by underground objects during operation. This approach addressed the scarcity of cmopd image datasets and reduced the interference from negative underground environments. In the feature extraction stage, the partial convolution of the backbone network C2f module is replaced by deformable convolution, and a novel C2f_DCN module is designed. This enhancement increased the dynamic adjustment capability of the target receptive field for cmopd with different passenger-carrying statuses, allowing the model to capture different scale information and better learn the coupled features of cmopd and miners. As a result, the model became more adaptable to various sitting postures of miners and improved its ability to identify cmopd targets with different passenger-carrying statuses. In the feature fusion stage, a path aggregation network with a coordinate attention mechanism (CLC−PAN−CA) was proposed to achieve cross-level contat of features and adaptively capture the contextual information of cmopd. The CLC−PAN−CA module effectively integrated multi-scale features and improved the accuracy of cmopd recognition. The experimental results show that the proposed model achieves a precision of 95.8%, which is 7.4% higher than the baseline model. The recall is 93.3%, representing an improvement of 9.8%, and the mean average precision is 95.6%, indicating a 7.7% increase. Furthermore, the model parameters and size are only 3.1 M and 6.1 MB, respectively. The recognition speed is 71 frames per second Compare to a variety of mainstream single-stage two-stage detection models, the proposed model demonstrated effective identification of cmopd targets with and without passengers, significantly improved the accuracy of cmopd recognition, reduced false positives and false negatives, and exhibited faster recognition speed and better extraction of contextual information. The proposed algorithm can meet the requirements of practical inspection scenarios and provide a feasible method for accurate recognition of cmopd with different passenger-carrying statuses. Finally, the proposed cmopd intelligent recognition algorithm and the underground monitoring video stream were embedded into the designed cmopd intelligent recognition system. Partial implementation approaches for deploying the video media stream into the cmopd intelligent recognition system were provided. The concept of an end-to-end integrated cmopd intelligent recognition system, which integrates the dispatching system on the ground and the monitoring system underground, was proposed. This increases the expectations for intelligent inspection applications in coal mines and provides real-time warnings for the safe transportation of cmopd with passengers.