点击上方“机器学习与生成对抗网络”，关注星标

获取有趣、好玩的前沿干货！

作者丨Tom Hardy  来源丨3D视觉工坊

文献资料汇总：

https://github.com/Eatzhy/surface-defect-detection

综述：机器视觉表面缺陷检测综述

缺陷检测工具箱：

https://github.com/abin24/Saliency-detection-toolbox

基于深度学习方式

1、语义分割方式

https://github.com/Wslsdx/Deep-Learning-Approach-for-Surface-Defect-Detection

https://github.com/LeeWise9/Segmentation-Based-Surface-Defect-Detection

https://github.com/CristinaMa0917/Defects_Detection_MaskRCNN

2、目标检测方式

https://github.com/YeahHuang/Al_surface_defect_detection

3、基于GAN

https://github.com/hukefei/GAN-defect

4、不同行业应用

1）PCB

https://github.com/Ixiaohuihuihui/Tiny-Defect-Detection-for-PCB

https://github.com/chinthysl/AXI_PCB_defect_detection

https://github.com/gustavo95/pcb-defect-detection

2）钢材缺陷检测

https://github.com/khornlund/severstal-steel-defect-detection

https://github.com/Diyago/Severstal-Steel-Defect-Detection

https://github.com/toandaominh1997/Steel-Defect-Detection

https://github.com/rook0falcon/steel-defect-detection

3）胶囊缺陷检测

https://github.com/TSjianjiao/Defect-Detection-with-tensorflow

4）电池缺陷检测

https://github.com/cdeldon/thermography

https://github.com/evip/ButtonDefectDetection

5）织物缺陷检测

https://github.com/weningerleon/TextileDefectDetection

https://github.com/freedom-kevin/defect_detection

https://github.com/Johncheng1/Fabric-defect-detection

https://github.com/luissen/SSDT-A-single-shot-detector-for-PCB--defects

https://github.com/wangerniuniu/FabricDefectDetection

https://github.com/mynameiswangshiyi/AE-BP-fabric-defect-detection

6）水果和蔬菜缺陷检测

https://github.com/shyamsuresh14/Detection-of-defects-in-fruits-and-vegetables

其它

https://github.com/skokec/segdec-net-jim2019

https://github.com/zwb204/Industrial_defect_detection

https://github.com/wuziheng/SiliconWaferDefectDetection

https://github.com/qiucongying/Mcue

https://github.com/yjphhw/SACNN

缺陷检测数据集

https://github.com/abin24/Surface-Inspection-defect-detection-dataset

https://github.com/Eatzhy/Surface-defect-Detection-dataset

猜您喜欢：

超100篇！CVPR 2020最全GAN论文梳理汇总！

拆解组新的GAN：解耦表征MixNMatch

StarGAN第2版：多域多样性图像生成

附下载 | 《可解释的机器学习》中文版

附下载 |《TensorFlow 2.0 深度学习算法实战》

附下载 |《计算机视觉中的数学方法》分享

《基于深度学习的表面缺陷检测方法综述》

《零样本图像分类综述: 十年进展》

《基于深度神经网络的少样本学习综述》