Detection system of miners' wearable security equipments based on SSD-MobileNet

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.