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%.
In order to improve the accuracy of road surface disease detection, an artificial intelligence-based highway pavement 3D information perception software system scheme is proposed. This article first proposed the hardware composition of the system and the overall system architecture scheme, and introduced in detail the method of implementing high-speed acquisition and storage software system functions. Subsequently, a collaborative control algorithm for coordinating and orderly working the sensors of the three-dimensional road inspection vehicle is proposed, which can avoid conflicts in the work of the sensors. Finally, the specific algorithm flow of the highway pavement 3D information perception software system is proposed. The actual experimental results show that the system can extract the highway pavement 3D information at high speed and accurately.
KEYWORDS: RGB color model, Digital filtering, 3D modeling, Image filtering, Optical filters, Detection and tracking algorithms, Chromium, Performance modeling, Roads, Global Positioning System
In order to identify the status of traffic lights in urban traffic scenes effectively, a recognition method of traffic lights using HSV color space model is proposed in this paper. Firstly, the median filter and the light compensation algorithm are used to preprocess images of urban traffic scenes. Secondly, the template matching method of traffic lights and the Bhattacharyya coefficient are used to detection of the traffic lights area in images of traffic scenes. Finally, the status of traffic lights in urban traffic scenes are identified using HSV color space model. The experimental results show that the proposed recognition method of traffic lights using HSV color space model offers the best performance than RGB color space model and YCbCr color space model. The recognition accuracies of red, green and yellow traffic lights are 96.67%, 95.0% and 88.67%, respectively.
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