KEYWORDS: Point clouds, Roads, Feature extraction, Covariance matrices, 3D modeling, Data modeling, Asphalt pavements, 3D image reconstruction, Visualization, Contour extraction
To address the problem of low accuracy of the traditional 3D laser point cloud pavement potholes extraction algorithm, this paper proposes a road potholes extraction method based on the improved normal vector distance. Then, the normal vector distance of the sampled point is obtained by calculating the distance from the sampled point to the tangent plane of the quadratic surface of the local neighborhood, which is used to describe the 3D features of the sampled point; then, the normal vector distance is diluted and the features are extracted by the Douglas- Peucker algorithm. Finally, the Alpha-Shape algorithm is used to further fit the pit contour and remove the internal noise points, and the B-sample interpolation is used again to fit the boundary of the extracted pit contour to obtain the final pit boundary point cloud collection. The final experimental results show that the average relative error of the pit depth is 3.70%, which is 9.31% higher than that of the traditional method, the average relative error of the extracted pit area is 3.82%, which is 7.33% higher than that of the traditional method, and the average relative error of the extracted pit perimeter is 1.41%. The experimental results show that the method in this paper can extract the pavement pothole features well and improve the performance in terms of the accuracy of pothole feature description compared with the traditional method.
Due to the low foreground brightness and distorted background brightness of traffic backlight images, a technique of fusing LIME and improved histogram equalization is proposed to eliminate the backlight phenomenon in images. Firstly, the input image is segmented into two parts, foreground and background, using the maximum interclass variance method. Then LIME method is used globally for the backlit image to enhance the foreground brightness while maintaining the color distortion, and only the foreground part of the processed image is retained. Then the pixel value distribution of the background part is calculated separately, and the global histogram equalization results on the three RGB channels are mapped one by one to the corresponding limited interval, which improves the contrast of the background. Finally, the Canny operator is used to detect the black edges at the front background stitching, and three adaptive filtering templates are generated based on the black edges to perform step-by-step mean filtering on the black edges, eliminating the black edges and improving the visual quality of the image. The average metrics of the proposed method on the laboratory selfconstructed CHD_B dataset are, respectively, NIQE of 5.37, BRISQUE of 47.74, average gradient of 0.79, information entropy of 6.61, and average running time of 11.38s, which are synthetically better than the current backlight image enhancement methods and have better visual quality and visibility
As one of the common road diseases, the accurate detection of potholes during inspections can help to make timely maintenance measures, which will greatly save road maintenance costs and reduce the incidence of traffic accidents. In order to improve the accuracy and timeliness of pothole detection and to facilitate the development of disease maintenance, the YOLOv7 model is improved. Firstly, the Efficient convolution operator (DSConv) is introduced to reconstruct the Backbone and Head parts of YOLOv7 to reduce the computational effort of the original YOLOv7 model and improve the detection speed of the model. Secondly, the SE attention mechanism is incorporated into the model to improve the model's ability to extract features from potholes. Finally, the latest v3 version of Wise-IoU is introduced as the loss function of the improved model to reduce the impact caused by sample annotation. The accuracy and mAP of the improved model improved by 3.04% and 1.34% respectively compared to the original YOLOv7 model, and the number of FLOPS of the model decreased from 105.1 to 48.8. The results show that the proposed improved method can effectively improve the speed and accuracy of the YOLOv7 model in detecting potholes, and is advanced compared to the current mainstream target detection algorithms.
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