Various 3D reconstruction methods have enabled civil engineers to detect damage on a road surface. To achieve the millimetre accuracy required for road condition assessment, a disparity map with subpixel resolution needs to be used. However, none of the existing stereo matching algorithms are specially suitable for the reconstruction of the road surface. Hence in this paper, we propose a novel dense subpixel disparity estimation algorithm with high computational efficiency and robustness. This is achieved by first transforming the perspective view of the target frame into the reference view, which not only increases the accuracy of the block matching for the road surface but also improves the processing speed. The disparities are then estimated iteratively using our previously published algorithm where the search range is propagated from three estimated neighbouring disparities. Since the search range is obtained from the previous iteration, errors may occur when the propagated search range is not sufficient. Therefore, a correlation maxima verification is performed to rectify this issue, and the subpixel resolution is achieved by conducting a parabola interpolation enhancement. Furthermore, a novel disparity global refinement approach developed from the Markov Random Fields and Fast Bilateral Stereo is introduced to further improve the accuracy of the estimated disparity map, where disparities are updated iteratively by minimising the energy function that is related to their interpolated correlation polynomials. The algorithm is implemented in C language with a near real-time performance. The experimental results illustrate that the absolute error of the reconstruction varies from 0.1 mm to 3 mm.

The detection of multiple curved lane markings is still a challenge for advanced driver assistance systems today, due to interference such as road markings and shadows cast by roadside structures and vehicles. The vanishing point Vpcontains the global information of the road image. Hence, Vp-based lane detection algorithms are quite insensitive to interference. When curved lanes are assumed, Vpshifts with respect to the rows of the image. In this paper, a Vpfor each individual row of the image is estimated by first extracting a Vpy(vertical position of the Vp) for each individual row of the image from the v-disparity. Then, based on the estimated Vpy's, a 2-D Vpx(horizontal position of the Vp) accumulator is efficiently formed. Thus, by globally optimizing this 2-D Vpxaccumulator, globally optimum Vps for the road image are extracted. Then, estimated Vps are utilized for multiple curved lane marking detection on nonflat road surfaces. The resultant system achieves a detection rate of 99% in 1862 frames of six stereo vision test sequences.

In this paper, a stereo vision based pothole detection system is proposed. Using the disparity map generated from an efficient disparity calculation algorithm, potholes can be detected by their distance from the fitted quadratic road surface. The system produces the size, volume and position of the potholes which allows the pothole repair to be prioritised according to its severity. The quadratic road surface model allows for camera orientation variation, road drainage and up/down hill gradients. Experimental results show robust detection in various scenarios.

This paper presents a local disparity calculation algorithm on calibrated stereo images based on cost aggregation. Unlike most of the existing cost aggregation methods which are mainly based on the grouping of colour similarities, the proposed algorithm is grouped by local cost similarities. The proposed algorithm also applies a bilateral filter to enhance the normalised cost volume and, then, uses the winner-take-all technique to select the correspondence candidates. Finally, a quadratic polynomial interpolation is performed using the candidates and their neighbourhood values to achieve sub-pixel disparity resolution. The experimental results indicate that the proposed algorithm is able to provide dense disparity maps with sub-pixel resolution and achieves better accuracy compared to two similar stereo matching algorithms.

This paper presents an obstacle detection system which is robust to non-flat road surface and interference of illumination. A 3D camera is used to generate depth information without the need of camera calibration. The depth map is then transformed into U-V-disparity domain, where obstacles and ground surface are projected as lines. Hough Transform is employed to extract line features; it has been modified to fit the characteristic of the U-V-disparity in order to boost the speed and accuracy. In addition, steerable filters are applied to the u-v histogram before Hough Transform for noise reduction. By categorising extracted lines according to their position and posture, road surface and on-road obstacles can be detected. Finally, results obtained using both U and V disparity maps are combined to eliminate road side surface and post processing. Experiments show that the proposed system is Able to detect obstacles accurately under various environments.