Research on safety helmet detection method based on convolutional neural network

By combining artificial neural network with deep learning technology, convolution neural network is characterized by local perception, adaptive feature extraction and end-to-end application, etc., and it has been used in image recognition and target detection more and more in recent years. Problems are existing widely in the traditional safety helmet detection algorithm generally such as the severe background interference, complex computing, high time-complexity and largely fluctuant accuracy. A detective method for safety helmet based on deep convolution network was proposed in this paper, which first decoded the acquired video monitoring data for a number of YUV images, then to determine the detecting area in the image, and transfer the YUV component image in the detecting area to the RGB image data; then in which to determine the training set and detecting set; finally, based on the constructed convolution neural network model to compute and process to acquire the ultimate detective results.