Tile Surface Defect Detection Based on Improved Faster R-CNN

Authors

  • Xuan Che
  • Wenzhong Zhu

DOI:

https://doi.org/10.54097/ajst.v7i1.11366

Keywords:

Defect detection, Tile defects, Faster-RCNN, BiFPN.

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.

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References

Huang HuiNing. Global Ceramic Tile Development Status and Implications[J].Foshan Ceramics.Vol. 25(2015),p.1-11.

SHOCKLETTI.Review on application of surface defect detection[J].Electronic Technology.Vol. 49(2020),p.189-191.

Pu YuXiang.Design of Bottle Cap Visual Inspection System Based on Machine Vision[J].Light Textile Industry and Technology. Vol. 49(2020),p.30-33.

Yang Cui.Speckled Micro-defects Detection Based on Deep Neural Network Learning[J].Journal of Anqing Normal University(Natural Science Edition).Vol.28(2022)No.4,p.51-56.

Biradara M, Shiparamattia B,Patilb B .Fabric Defect Detection Using Deep Convol-utional Neural Network[J].Optical Memory and Neural Networks.Vol. 30(2021),p.250-256

Girshick R, Donahue J , Darrell T , et al.Rich feature hierarchies for accurate object detection and semantic segmentation[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Beijing,Jul 6,p.580-587.

Ren S Q, He K M , Girshick R, et al.Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks[J].Neural Information Processing Systems. 6(2017), p.1137-1149.

Redmon J , Divvala S, Girshick R,et al.You Only Look Once: Unified, Real-Time Object Detection[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Santiago,Dec 7-13,p.779-788.

Lin T, Goyal P, Girshick R, He K M, et al. Focal Loss for Dense Object Detection[C]. 2017 IEEE International Conference on Computer Vision. Venice.Italy,Oct 22-29, p.2999-3007.

Zhang ZuoRen.ResNet-Based Model for Autonomous Vehicles Trajectory Prediction [C].2021 IEEE International Conference on Consumer Electronics and Computer Engineering . Guangzhou,Oct 3,p.565-568.

Mingxing Tan, Ruoming Pang, Quoc V. Le.EfficientDet: Scalable and Efficient Object Detection[C].Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2020).Seattle,June 14-19,p.10781-10790

Andrew G. Howard, Menglong Zhu, Bo Chen,et al.MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.Goggle.

Bodla N,Singh B,Chellappa R.Soft-NMS Im-proving Object Detection with One Line of Code [C].2017 IEEE International Conference on Computer Vision.Venice,Aug 9,p.5562-5570.

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Published

23-08-2023

Issue

Section

Articles

How to Cite

Tile Surface Defect Detection Based on Improved Faster R-CNN. (2023). Academic Journal of Science and Technology, 7(1), 184-190. https://doi.org/10.54097/ajst.v7i1.11366

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