In order to improve the accuracy of road surface disease detection, the development technology of unmanned aerial vehicle (UAV) in the intelligent transportation field are analyzed. Firstly, the framework of pavement disease recognition and perception system based on UAV is constructed; Secondly, the pavement image data collection experiment is carried out with Yuneec H520 UAV, and the pavement disease image preprocessing technology based on wavelet threshold transform is analyzed; Thirdly, the pavement disease image preprocessing technology based on the DPM is studied, and the recognition method of pavement disease based on VGG-16 neural net-work model is proposed. Theoretical analysis and experimental results show that the method of pavement disease identification based on VGG-16 neural network model has high classification accuracy, and its classification accuracy is better than 90％.
At present, academic research mainly focuses on detecting driver fatigue and distraction through the driver's eyes and head. But there are few studies on detecting driving behavior through the head, hands and even the body, most of which use the skin color detection method to extract a single full-image pixel as a feature and the dimension is too large, problems such as instantaneous region overlap and partial occlusion occur inevitably in the detection process, thereby affecting the detection accuracy. In this paper, we propose a driving posture detection method based on video and skin color region distance. The image features are represented by extracting the skin color region centroid coordinates of the sampled images from videos and converting them into feature distances. Then the BP neural network is used to implement the identification and classification of driving behavior, which can effectively improve the detection rate of the driving behavior, and finally realize the real-time warning of the driving process.