Abstract: In order to solve the problems existing in current coal mine monitoring, such as excessive manual intervention and low monitoring efficiency, a single-stage coal mine target detector was established based on RetinaNet, and the key parameters of detection were determined through experiments and the detection effect was verified. The experimental results show that the RetinaNet target detector can automatically detect and extract key objects such as personnel, and the overall performance can meet the requirements of coal mine monitoring; RetinaNet target detector can realize accurate detection of target objects under poor environmental conditions, which has reached a relatively ideal level for human identification; the image recognition model based on the existing data can not identify various types of coal mine machinery and equipment well. The realization of relevant functions of RetinaNet target detector depends on the establishment of professional image data set and the accurate training model to explore the depth value of data.