Reliable detection of obstacles around an autonomous vehicle is essential to avoid potential collision and ensure safe driving. However, a vast majority of existing systems are mainly focused on detecting large obstacles such as vehicles, pedestrians, and so on. Detection of small obstacles such as road debris, which pose a serious potential threat are often overlooked. In this article, a novel stereo vision-based road debris detection algorithm is proposed that detects debris on the road surfaces and estimates their height accurately. Moreover, a collision warning system that could warn the driver of an imminent crash by using 3D information of detected debris has been studied. A novel feature-based classifier that uses a combination of strong and weak features has been developed for the proposed algorithm, which identifies debris from selected candidates and calculates its height. 3D information of detected debris and vehicle’s speed are used in the collision warning system to warn the driver to safely maneuver the vehicle. The performance of the proposed algorithm has been evaluated by implementing it on a passenger vehicle. Experimental results confirm that the proposed algorithm can successfully detect debris of ≥5 cm height for up to a 22 m distance with an accuracy of 90%. Moreover, the debris detection algorithm runs at 20 Hz in a commercially available stereo camera making it suitable for real-time applications in commercial vehicles.
Surface quality of the piston rod is very important for the durability of the shock absorbers. However, currently in production, it is very difficult to use the available systems to inspect the whole rod surface online, in real time (∼4s), with a high sensitivity and accuracy. To overcome this, in this paper, an online automatic rod inspection system has been developed, which allows to inspect the whole rod surface in 4s, with a sizing accuracy around 75%, and detection accuracy <90%. An integrated software is used for rod rotation control, image capturing, image processing, and decision making. Convolutional Neural Network is used to processing the 360° surface image with a high accuracy, eliminating errors caused by environmental lighting. A new method based on aspect ratio and size information is used for defects classification. Experimental results show that the system is capable of detecting defects as small as 25μm and differentiating nodule, dent, and scratch with a processing time around 4s per rod.
<div class="section abstract"><div class="htmlview paragraph">Stereo vision based sensing systems have gained significant attention during the last two decades due to its reliable and accurate obstacle detection and recognition capabilities. Such systems with advanced processing units are now widely used in partially automated vehicles to improve passengers’ safety and comfort level. A predictive suspension control system that could provide better ride comfort and safety to the passengers by detecting potholes in advance and control the suspension system accordingly has been investigated in this study. Potholes can become serious safety hazard and can often cause discomfort if not detected and maneuvered at the right time. In this paper, a novel stereo vision based pothole detection system is proposed that detects pothole and calculates its depth accurately. In this proposed system, region of interest (ROI) of potential pothole candidates are selected utilizing intensity image and disparity image which is created using a pair of stereo images captured by a stereo camera. An intensity-depth based classifier has been developed which identifies the potholes from selected candidates and calculates its depth. Finally, 3D information of detected potholes is used to control the damping coefficient of the suspension system to improve the ride quality. The performance of the proposed pothole detection system has been evaluated using approximately 3.5 hours of driving video data captured with a frame rate of 20 frames/second. Experimental results show that, the accuracy of the proposed pothole detection system is about 84% and can detect pothole with ≥ 5 cm depth. Moreover, in-vehicle experiments confirm that the ride quality can be improved of about 16% utilizing the pothole detection system. The proposed system can be implemented for real-time applications in commercial vehicles and could provide significant benefits by improving safety and ride quality.</div></div>
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