# Detecting and Localizing Dents on Vehicle Bodies Using Region-Based Convolutional Neural Network
###### Abstract  
Detection and localization of the dents on a vehicle body that occurs during manufacturing is critical to achieve the appearance quality of a new vehicle. This study proposes a region-based convolutional neural network (R-CNN) to detect and localize dents for a vehicle body inspection. For a better feature extraction, this study employed a lighting system, which can highlight dents on an image by projecting the Mach bands (bright-dark stripes). The R-CNN was trained using the highlighted images by the Mach bands, and heat-maps were prepared with the classification scores estimated from the R-CNN to localize dents. This study applied the proposed R-CNN to the inspection of dents on the surface of a car body and quantitatively analyzed its performances. The detection accuracy of the dents was 98.5% for the testing data set, and mean absolute error between the actual dents and estimated dents were 13.7 pixels, which were close to one another. The proposed R-CNN could be applied to detect and localize surface dents during the manufacture of vehicle bodies in the automobile industry. region-based convolutional neural network; Mach bands; vehicle body inspection; heat map; dent localization +
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the workstation. However, it may lead to eye fatigue in the long term and eventually deteriorating inspection performance, although such high illuminance can improve inspection accuracy in the short term [12]. A few studies have used image-processing techniques in vehicle exterior inspections in order to improve inspection accuracy as well as overcome the limitations of visual inspections; however, there is still room to improve the inspection accuracy for small defects (difficult to detect). [PERSON] and [PERSON] [3] and [PERSON] et al. [13] attempted to detect a vehicle exterior defect by calculating surface curvature characteristics from images measured with a 3D scanner and a 2D camera, respectively. However, they did not report the defect detection accuracy of their method. [PERSON] et al. [14] developed discriminant models including decision trees, fuzzy decision trees, neuro fuzzy classification, and mixed fuzzy rules with regard to the inspection variables (e.g., volume and depth) calculated from car body images acquired through a 3D scanner. Their results revealed that the decision trees exhibited the highest defect detection rate of 87.2%. Lastly, [PERSON] et al. [2] applied a support vector machine to the vehicle body images taken by a camera and reported an average defect detection rate of 98.8%; however, the study employed only simple and clear defects for testing enough to be detected by bare eyes. Neural networks have been widely employed in many areas, such as classification [15; 16; 17] and forecasting [18; 19]. Convolutional neural network (CNN), as a type of neural networks, has demonstrated preeminent performance toward detecting defects in various fields with good accuracy. [PERSON] et al. [15] established CNN models for different materials to detect defects and reported an average accuracy of 92.7%. [PERSON] et al. [16] developed a CNN model for detecting defects on rail surfaces and reported an accuracy of 92%. [PERSON] et al. [17] proposed a fast and robust CNN model for detecting defects on textured surfaces with an average accuracy of 99.8%. However, CNN cannot pin-point the locations of defects on an image (called localization). More recently, a few studies with region-based CNN (R-CNN) models has shown excellent performance in both detection and localization tasks. [PERSON] et al. [20] proposed an R-CNN algorithm to detect strawberry fruit using harvesting robot with reported accuracy of 95.78%. [PERSON] et al. [21] developed an R-CNN with 95.7% accuracy that can detect and localize a defect on the GDXray Castings dataset. However, a R-CNN model has not been utilized in detecting and localizing minor dents on vehicle body surfaces yet. This study developed an automatic method for detecting and localizing dents on a vehicle body surface with a R-CNN model.