Target detection of underground personnel based on deep convolutional neural network

In view of problems that human—centered video monitoring mode had limited duration, multiple scenes were difficult to monitor at the same time, and results of manual monitoring were not processed in time, target detection method of underground personnel based on deep convolutional neural network was proposed. Firstly, input image was scaled to a fixed size, and a feature map was formed after operation of deep convolutional neural network; then, a suggestion area was formed on the feature map through area suggestion network, the suggestion area was pooled into a unified size which was sent to full connection layer for operation; finally, the best suggestion area was selected according to probability score, and the required target detection box was automatically generated. The test results show that the method can successfully detect head of underground personnel with an accuracy rate of 87.6%.