Defect-Detection-yolov5

This project is based on ultralytics' yolov5 project. The original project can be found here

This model are designed for a subproject of defect detection. It can detect Multiple equipment components in single image or video stream.

Requirements

• Python 3.8 or later with all [requirements.txt]
• PyTorch 1.7.0 or later with CUDA and torchvision

create virtual environment

bash python -m venv Defect-Detection source Defect-Detection/bin/activate

install requirements

bash cd $project_path pip install -r requirements.txt

Raw data processing

Data should store in yolov5-format dir tree as follow:

markdown yolov5_data sub_yolo_data images train val labels train val

Where images should be .jpg format and labels should be yolo .txt format.

For raw data in folder, way to process data is as follow:

Seperate mixed data

In each data folder,images(.jpg files and .JPG files) and labels(.txt files) are mixed together. We need to seperate them into two folders and rename them.

Code for replacing space with underscore

bash data_dir="~/Documents/Defect-Detection-yolov5/data/raw/" cd $data_dir for f in *; do mv "$f" `echo $f | tr ' ' '_'`; done

Code for seperating images and labels

bash data_dir="~/Documents/Defect-Detection-yolov5/data/raw/" touch mvjpg.txt touch mvxml.txt find $data_dir -iregex .*\.jpg > mvjpg.txt find $data_dir -iregex .*\.xml > mvxml.txt mkdir $data_dir/images mkdir $data_dir/labels for i in $(cat mvjpg.txt); do (mv $i $data_dir/images); done for i in $(cat mvxml.txt); do (mv $i $data_dir/labels); done rm mvjpg.txt rm mvxml.txt

Deal with images with wrong label in the dataset

Labeling errors are common in the dataset, so we need to deal with them. For some unknown reason, labels of some images are rotated by 90 degrees(seems randomly clockwise or counterclockwise).

bash # find the images with wrong label python data/wrong_size.py

The outout is a txt file with the path of images with wrong label.including rotated images and images with incorrect label for other reasons.then seperate them from the dataset.

bash data_dir="/Users/wzilai/Documents/Defect-Detection-yolov5/data/raw/" mkdir $data_dir/bad_examples/images mkdir $data_dir/bad_examples/labels for i in $(cat $data_dir/wrong_size.txt); do mv $data_dir/images/$i $data_dir/bad_examples/images; done for i in $(cat $data_dir/wrong_size.txt); do mv $data_dir/labels/$i $data_dir/bad_examples/labels; done

You can fix the label of these images manully or just use the remaining images to train the model.

Change label format

raw labels are stored in xml format,but yolo model need input in yolo-format. prepare.py can change xml format to yolo-format and separate data into train set and validation set. before run prepare.py, you need to change class name and yolo-format dir name in prepare.py. dir create is not fully-automatic in prepare.py, you need to create dir by yourself for now.

data dir tree

markdown yolov5_data sub_yolo_data images train val labels train val

Create yaml file

yaml file is used to record the project information. After run prepare.py, create yaml file for dataset, then change train/test image path and class name in yaml file. train.py will automatically read yaml file, and find labels.

Sample yolov5 yaml file

yaml path: ./data/dataset #train/val/test dir are subdirs of path train: images/train val: images/val test: image/test #can be empty if no test set #Number of classes nc: 3 #Classes names: 0: cat 1: dog 2: person

training

track and visualize

yolov5

python !pip install comet_ml

or

python !pip install wandb import wandb wandb.login()

train

```bash python train.py --img 640 //image size --batch 16 //batch size --epochs 300 //epochs --data withoutddityxtg.yaml //yaml file path --weights yolov5s.pt //pretrained weights --device 0 //gpu id, -1 for cpu, 0,1,2,3,... for multi-gpu --hyp data/hyp.scratch-low.yaml //hyperparameters

```

you can change hyperparameters in hyp.yaml file. result for 300 epochs:

markdown Class Images Instances P R mAP50 all 182 179 0.516 0.583 0.541 0.282 cysb_sgz 182 3 0.174 0.667 0.159 0.0843 SF6ylb 182 3 0.556 0.333 0.34 0.238 drq 182 31 0.487 0.516 0.344 0.14 ecjxh 182 6 0.491 0.324 0.456 0.254 drqgd 182 5 0.295 0.6 0.445 0.248 cysb_lqq 182 23 0.475 0.522 0.508 0.208 cysb_qtjdq 182 12 0.356 0.583 0.547 0.221 xldlb 182 15 0.906 0.733 0.778 0.439 ywj 182 2 1 0.977 0.995 0.505 jdyxx 182 6 0.352 0.333 0.283 0.231 bmwh 182 2 0.798 0.5 0.828 0.133 xmbhzc 182 1 0.38 1 0.995 0.796 pzq 182 22 0.317 0.318 0.256 0.115 jyh 182 13 0.923 0.846 0.853 0.472 ywc 182 10 0.086 0.2 0.052 0.015 cysb_cyg 182 25 0.657 0.88 0.814 0.415

Detect

bash !python detect.py --weights ~/Defect-Detection-yolov5/runs/train/exp21/weights/best.pt --source your_source

markdown Usage - sources: --source 0 # webcam img.jpg # image vid.mp4 # video screen # screenshot path/ # directory list.txt # list of images list.streams # list of streams 'path/*.jpg' # glob 'https://youtu.be/Zgi9g1ksQHc' # YouTube 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream

Example