Mask Detection Based On Efficient-YOLO

摘要

COVID-19 can be transmitted by respiratory droplets and other means. With the normalization of epidemic prevention and control, mask detection should be performed when entering public places with heavy human flow. A mask detection method for robot was proposed in this paper to prevent cross-infection of the detection personnel. This paper used an improved algorithm of YOLO. First, this article is based on an improved backbone Darknet-53, and add the EfficientNet attention mechanism as the basic network, reduces the consumption of calculation and improve the training speed, secondly the original IoU replaced by DIoU, improves the prediction precision of the border, finally combining with robot platform, according to more than 6000 pieces of VOC data format homemade model training and test data set. The robot detects the target based on the camera it carries, and then guides it to the next step. The experimental results show that the mAP of the algorithm is 86.92% on the self-made verification set, and the preset process can be realized in the actual detection process by using the robot.