Surface-Defect Segmentation using U-shaped Inverted Residuals

Surface-Defect segmentation plays an important role. It is very necessary to detect on products during production process. Though there are several previous works in surface-defect segmentation, it needs high handcraft skill. We address automatic segmentation algorithm in surface using U-shape inverted residuals to achieve end-to-end learning network. A proposed method is data acquisition, surface-defect segmentation network creation and training. First, the experimental image is augmented by image processing technique, such as rotation, flip, translation, skew and zoom in, which is randomly augmented. Second, U-shape inverted residuals segmentation network is created by changing backbone of encoder and reconstructs decoder by inverted of encoder in order to improve performance of segmentation network. In the final step, the training step of the proposed network is set. To evaluate the performance of the proposed network, each plastic hose tip and dental caries 10,000 image are used to compare between proposed network and Unet [15]. From the experiment, Dice score and IoU are 77.11% and 62.75% in plastic hose tip, respectively. In dental caries problem, Dice score and IoU are 84.16% and 72.65%, respectively. The results show that the proposed network is satisfactory and able to be improved for higher performance. Advantages of the method are that it avoids handcraft feature extraction and is automatically learning.