Abstract: In view of problems of supervision difficulties caused by complexity of mine personnel flow, and that security equipments cannot be effectively worn due to weak safety awareness of underground personnel, a detection system of miners' wearable security equipments based on SSD-MobileNet was designed. Feature extraction network VGG16 of SSD algorithm is replaced by MobileNet network to construct SSD-MobileNet algorithm model. Photo data set of miners' wearable security equipments is made according to VOC2007 data set standard and used to train the SSD-MobileNet algorithm model. The SSD-MobileNet algorithm is used to identify eight wearable security equipments for miners (hardhats, dust masks, overalls, work boots, flashlights, self-rescuing devices, positioning cards, anti-back clamps). The highest confidence of photos of miner with multiple angles is used for comprehensive determination of whether a security equipment is worn and the highest confidence threshold is set at 75%. The test results show that the system can detect wearing condition of miners' security equipments in real time and accurately, and has good stability and anti-interference.