Python-基于分割的深度学习表面缺陷检测方法的一个Tensorflow实现

# Deep-Learning-Approach-for-Surface-Defect-Detection A Tensorflow implementation of "**Segmentation-Based Deep-Learning Approach for Surface-Defect Detection**" （***秒杀 DeepLabV3+ 和 Unet 的 缺陷检测网络***） The author submitted the paper to Journal of Intelligent Manufacturing (https://link.springer.com/article/10.1007/s10845-019-01476-x), where it was published In May 2019 . # The test environment ``` python 3.6 cuda 9.0 cudnn 7.1.4 Tensorflow 1.12 ``` # You should know I used the Dataset used in the papar, you can download [KolektorSDD](https://www.vicos.si/Downloads/KolektorSDD) here. If you train you own datset ,you should change the dataset interfence for you dataset. You can refer to the [paper](https://link.springer.com/article/10.1007/s10845-019-01476-x) for details of the experiment. # my experimental results on KolektorSDD **Notes:** the first 30 subfolders are used as training sets, the remaining 20 for testing. Although, I did not strictly follow the params of the papar , I still got a good result. ``` 2019-05-21 09:20:54,634 - utils - INFO - total number of testing samples = 160 2019-05-21 09:20:54,634 - utils - INFO - positive = 22 2019-05-21 09:20:54,634 - utils - INFO - negative = 138 2019-05-21 09:20:54,634 - utils - INFO - TP = 21 2019-05-21 09:20:54,634 - utils - INFO - NP = 0 2019-05-21 09:20:54,634 - utils - INFO - TN = 138 2019-05-21 09:20:54,635 - utils - INFO - FN = 1 2019-05-21 09:20:54,635 - utils - INFO - accuracy(准确率) = 0.9938 2019-05-21 09:20:54,635 - utils - INFO - prescision（查准率） = 1.0000 2019-05-21 09:20:54,635 - utils - INFO - recall（查全率） = 0.9545 ``` **visualization:**  # testing the KolektorSDD After downloading the KolektorSDD and changing the param[data_dir] ``` python run.py --test ``` Then you can find the result in the "/visulaiation/test" and "Log/*.txt" # training the KolektorSDD **First, only the segmentation network is independently trained, then the weights for the segmentation network are frozen and only the decision network layers are trained.** training the segment network ``` python run.py --train_segment ``` training the decision network ``` python run.py --train_decision ``` training the total network( not good） ``` python run.py --train_total ```