基于SSD-MobileNet的矿工安保穿戴设备检测系统

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

针对因矿山人员流动复杂性导致监管困难及因下井人员安全意识不强导致不能有效穿戴安保设备等问题,设计了基于SSD-MobileNet的矿工安保穿戴设备检测系统。将SSD算法的特征提取网络VGG16替换成MobileNet网络,构建了SSD-MobileNet算法模型;按照VOC2007数据集标准制作矿工安保穿戴设备的照片数据集,对SSD-MobileNet算法模型进行训练;采用SSD-MobileNet算法识别矿工8件安保穿戴设备(安全帽、防尘面具、工作服、工作靴、手电筒、自救器、定位卡、防砸背夹),根据矿工多张不同角度的照片的最高置信度结果综合判定是否穿戴了某件安保设备,并将最高置信度阈值设置为75%。测试结果表明,该系统能够实时、准确地检测出矿工安保设备的穿戴情况,且具有良好的稳定性和抗干扰性。

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.