An application of a bio-inspired visual attention model for the localization of vehicle parts

• A two-step approach localizes vehicle parts using a bio-inspired visual attention.
• Bounding-boxes roughly locate parts of interest in the saliency map.
• Average bounding-box localization rates of 99.8% are obtained for different parts.
• Active contour models capture the boundaries of each vehicle part within each bounding box.
• A large number of parts can be identified in different views of a vehicle.

The automated servicing of vehicles is becoming more and more a reality in today's world. While certain operations, such as car washing, require only a rough model of the surface of a vehicle, other operations, such as changing of a wheel or filling the gas tank, require a correct localization of the different parts of the vehicle on which operations are to be performed. The paper describes a two-step approach to localize vehicle parts over the surface of a vehicle in front, rear and lateral views capitalizing on a novel approach based on bio-inspired visual attention. First, bounding-boxes are determined based on a model of human visual attention to roughly locate parts of interest. Second, the bounding-boxes are further searched to finely tune and better capture the boundaries of each vehicle part by means of active contour models. The proposed method obtains average bounding-box localization rates over 99.8% for different vehicle parts on a dataset of 120 vehicles belonging to sedan, SUV and wagon categories. Moreover, it allows, with the addition of the active contour models, for a more complete and accurate description of vehicle parts contours than other state-of-the-art solutions. This research work is contributing to the implementation of an automated industrial system for vehicle inspection.

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