Detecting and Localizing Dents on Vehicle Bodies Using Region-Based Convolutional Neural Network

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.

Second-Order Mach Bands, Chevreul, and Craik-O’Brien-Cornsweet Illusions

Second-order texture illusions, corresponding to Mach bands, Chevreul, and Craik-O’Brien-Cornsweet illusions in brightness perception, are generated by replacing luminance modulations in the classic stimuli with modulations of texture contrast. Whereas the classic (first-order) illusions exhibit changes in lightness or darkness near boundaries, the second-order stimuli exhibit analogous perceptual effects that are increases or decreases in apparent texture contrast with no concomitant change in apparent brightness. The magnitudes of the second-order texture-contrast changes are comparable to brightness changes in the classic first-order illusions. These results indicate that second-order (texture) illusions involve spatial interactions that are remarkably similar to those in first-order (luminance) processing.