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Abstract-This paper presents a method for the visual detection of parts of interest on the outer surface of vehicles. The proposed method combines computer vision techniques and machine learning algorithms to process images of lateral views of automobiles. The aim of this approach is to determine the location of a set of car parts in ordinary scenes. The approach can be used in the intelligent transportation industry to construct advanced monitoring and security applications. The key contributions of this work are the introduction of a methodology to locate multiple patterns in cluttered scenes of vehicles which makes use of a probabilistic technique to reduce false detection, and the proposal of a method for inferring the location of regions of interest using a priori knowledge. The results demonstrate excellent performance in the task of detecting up to fourteen different car parts over a vehicle.

This paper presents an approach for the automatic detection and fast 3D profiling of lateral body panels of vehicles. The work introduces a method to integrate raw streams from depth sensors in the task of 3D profiling and reconstruction and a methodology for the extrinsic calibration of a network of Kinect sensors. This sensing framework is intended for rapidly providing a robot with enough spatial information to interact with automobile panels using various tools. When a vehicle is positioned inside the defined scanning area, a collection of reference parts on the bodywork are automatically recognized from a mosaic of color images collected by a network of Kinect sensors distributed around the vehicle and a global frame of reference is set up. Sections of the depth information on one side of the vehicle are then collected, aligned, and merged into a global RGB-D model. Finally, a 3D triangular mesh modelling the body panels of the vehicle is automatically built. The approach has applications in the intelligent transportation industry, automated vehicle inspection, quality control, automatic car wash systems, automotive production lines, and scan alignment and interpretation.

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