Browsing by Author "Nienaber, Sonja"

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    Detecting potholes with monocular computer vision: A Performance evaluation of techniques
    (Stellenbosch : Stellenbosch University, 2016-03) Nienaber, Sonja; Booysen, M. J.; Kroon, R. S. (Steve); Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering.
    ENGLISH ABSTRACT: Potholes in road surfaces create problems for motorists and driverless vehicles. This is because the damage to the vehicle that can be caused by hitting a pothole with a vehicle can be costly and even dangerous. Previous works of other authors with respect to pothole detection did not investigate the limitations of the detection capabilities of their works such as the distance at which the potholes could be detected and often used footage where the camera was directly facing the road, thereby only having a viewing range of roughly 2-4 m. In order to complete this project, it was necessary to obtain suitable footage of potholes. The method for collecting the pothole footage can be seen as novel. The method included attaching a GoPro camera inside of a vehicle windscreen and photographing the road as the vehicle was driven around. This footage is, therefore, akin to a driver’s viewpoint of the road. This viewpoint is advantageous as it ensures that the maximum amount of the road can be photographed by the camera. By mounting the camera in this manner, it could potentially be possible to detect potholes before the vehicle reaches them as opposed to other works done where the camera was mounted to the rear of the vehicle. In the instance of a driverless vehicle, this would allow the vehicle to avoid hitting the pothole and would prevent damage to the vehicle. Due to the difficulty of detecting potholes, the footage was split into two different datasets namely, a simple scenario and a complex scenario. The simple scenario considered footage where the road lighting conditions were always open and clear. In this scenario, the road was extracted and only the extracted region was used in pothole detection algorithms. The complex scenario considered footage where the road lighting conditions were either open or contained mixed lighting conditions. Therefore, in this scenario, the input images were cropped to the suspected road region within the image. This region is rectangular and contains additional information along the sides of the image such as foliage etc. Image processing algorithms, as well as machine learning algorithms were deployed in this thesis to investigate the feasibility of pothole detection. The machine learning algorithms used, consisted of an LBP (Local Binary Pattern) cascade classifier and an SVM (Support Vector Machine) with HOG (Histogram of Oriented Gradients) features. The pothole locations were also analysed in terms of the relative distance that a pothole occurred from the camera. This process is known as depth estimation in monocular images, and this work allowed for determining the ranges at which pothole detection was more successful than others. The discrepancy in the results at the different depth ranges might indicate that different algorithms and classifiers need to be implemented for different ranges to increase the performance of the pothole detector. The final results of this project indicate that under certain conditions, it is possible to detect potholes with modest results.