X-Git-Url: https://gerrit.akraino.org/r/gitweb?a=blobdiff_plain;f=example-apps%2FPDD%2Fpcb-defect-detection%2FREADME.md;fp=example-apps%2FPDD%2Fpcb-defect-detection%2FREADME.md;h=a29e1eff6e78ea4a9d86e6c28a2b9f01048ddd6a;hb=a785567fb9acfc68536767d20f60ba917ae85aa1;hp=0000000000000000000000000000000000000000;hpb=94a133e696b9b2a7f73544462c2714986fa7ab4a;p=ealt-edge.git diff --git a/example-apps/PDD/pcb-defect-detection/README.md b/example-apps/PDD/pcb-defect-detection/README.md new file mode 100755 index 0000000..a29e1ef --- /dev/null +++ b/example-apps/PDD/pcb-defect-detection/README.md @@ -0,0 +1,117 @@ +# PCB-defect-detection +## Note +This project code is forked from https://github.com/DetectionTeamUCAS/FPN_Tensorflow. I have only made minor changes on this wonderful and clear project. Thanks for their perfect code. I can learn and apply it to a new problem. The Chinese blog is: https://blog.csdn.net/dlh_sycamore/article/details/87886198 +## PCB defect dataset +The Open Lab on Human Robot Interaction of Peking University has released the PCB defect dataset. If you need to do experiments on this dataset, please contact the dataset author. + +You can download at http://robotics.pkusz.edu.cn/resources/dataset/. or https://www.dropbox.com/s/32kolsaa45z2mpj/PCB_DATASET.zip?dl=0 + + +6 types of defects are made by photoshop, a graphics editor published by Adobe Systems. The defects defined in the dataset are: missing hole, mouse bite, open circuit, short, spur, and spurious copper. +For example: + +![1](a(missinghole).png) +### Dataset Update +The paper of this project will be update. +However, the defect images of raw dataset are high-resolution. +With the respect of such small dataset, data augmentation techniques are adopted before data training. The images are then cropped +into 600 × 600 sub-images, forming our training set and testing set with 9920 and 2508 images, respectively. + +### Some instructions: + +1、This augmented dataset is privately owned, if you want to use it in your paper, please contact me. + +2、If you have questions, please open an issue, I will reply as soon as possible. If you don't have some difficult problem about this project, maybe you **don't need** to send me an email and add wechat, just open an issue. + +3、Of course, if you have other research questions like dataset and defect detection, welcome to send me an email. + +4、If you use our code, please access this paper and cite: +Ding, Runwei, et al. "TDD-Net: A Tiny Defect Detection Network for Printed Circuit Boards." CAAI Transactions on Intelligence Technology (https://ieeexplore.ieee.org/document/8757220)(2019). + +5、Chinese Blog: https://blog.csdn.net/dlh_sycamore/article/details/87886198 + +## Download Model +Please download [resnet50_v1](http://download.tensorflow.org/models/resnet_v1_50_2016_08_28.tar.gz)、[resnet101_v1](http://download.tensorflow.org/models/resnet_v1_101_2016_08_28.tar.gz) pre-trained models on Imagenet, put it to $PATH_ROOT/data/pretrained_weights. +## My Development Environment +1、python2.7 (anaconda recommend) +2、CUDA Version 8.0.44 , CUDNN=5.1.10 +3、[opencv(cv2)](https://pypi.org/project/opencv-python/) +4、[tfplot](https://github.com/wookayin/tensorflow-plot) +5、tensorflow == 1.121 + +## Compile +``` +cd $PATH_ROOT/libs/box_utils/cython_utils +python setup.py build_ext --inplace +``` + +## Demo(available) + +Then download the corresponding weights: + +For Baiduyun: [weights](https://pan.baidu.com/s/1rvHjihG1fL499SqU28Nang). code：shac + +For Google driver: [weights](https://drive.google.com/drive/folders/1oEHrEslnM5DBKB-610j-gO-x9-e90VwR?usp=sharing) + + +And then put the trained weights into +``` +TRAINED_CKPT = os.path.join(ROOT_PATH, 'output/trained_weights') +``` +It depends on your project path. Please read '$PATH_ROOT/libs/configs/cfgs.py'. + +``` +cd $PATH_ROOT/tools +python inference.py --data_dir='/PATH/TO/THE/TO/BE/DETECTED/IMAGES/' + --save_dir='/PATH/TO/SAVE/RESULTS/' + --GPU='0' +``` +After running this code, you will get the detected image in your 'save_dir' path. + +Why didn't you get the correct output image when run the inference.py? + +**Note!!! If you didn't load the correct checkpoint but the pretrain model, you need to change the checkpoint file (FPN_Res101_0117_OHEM/checkpoint), because this file contains the wrong path, and you need to change it with your file path!!!** + +## Train +If you want to train your own data, please follow this project: https://github.com/DetectionTeamUCAS/FPN_Tensorflow + +1、Select a configuration file in the folder ($PATH_ROOT/libs/configs/) and copy its contents into cfgs.py. Modify parameters (such as CLASS_NUM, DATASET_NAME, VERSION, etc.) in $PATH_ROOT/libs/configs/cfgs.py + +2、Generate train and test floder + +``` +cd $PATH_ROOT/data/io/ +python divide_data.py +``` +You should check the image_path and xml_path in the 'divide_data.py' + +2、Make tfrecord + +(1)Modify parameters (such as VOC_dir, xml_dir, image_dir, dataset, etc.) in $PATH_ROOT/data/io/convert_data_to_tfrecord.py +``` +cd $PATH_ROOT/data/io/ +python convert_data_to_tfrecord.py +``` + +3、Train +``` +cd $PATH_ROOT/tools +python train.py +``` + +## Eval +``` +cd $PATH_ROOT/tools +python eval.py --eval_imgs='/PATH/TO/THE/TO/BE/EVALED/IMAGES/' + --annotation_dir='/PATH/TO/TEST/ANNOTATION/' + --GPU='0' +``` +After running this code, you will get the precision, recall and AP of per defect type. + +## Some results +[the more results](https://github.com/Ixiaohuihuihui/PCB-defect-detection/tree/master/tools/inference_results) +![1](01_missing_hole_01.jpg) +![2](04_mouse_bite_10.jpg) + +## The Precision and Recall curve (PR) +![3](TDD_results.jpg)