Tile Surface Defect Detection Based on Improved Faster R-CNN

Abstract

With the increasing population, there is a high demand for housing, commercial buildings, and lifestyle facilities. The development of modern manufacturing technology has led to higher quality control requirements for tiles. This article presents a ceramic tile defect detection method based on Faster R-CNN, primarily focused on detecting surface defects or cracks on tiles. The method replaces VGG16 with the ResNet-101 network as a new backbone and utilizes depth-wise separable convolution to address efficiency issues resulting from the backbone replacement. Finally, Soft-NMS is employed to optimize regression boxes and prevent missed detections. Experimental results demonstrate that the improved algorithm achieves a mAP of 70.4%, a 13.2% enhancement over the original algorithm, highlighting the effectiveness and feasibility of the proposed approach.