# spurgear-teeth-defects-detection-and-tracking-with-YOLOv7-DeepSort
**Repository Path**: zhangbeibei_page/spurgear-teeth-defects-detection-and-tracking-with-YOLOv7-DeepSort
## Basic Information
- **Project Name**: spurgear-teeth-defects-detection-and-tracking-with-YOLOv7-DeepSort
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-02-15
- **Last Updated**: 2025-02-15
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
# Inspection and Counting of Spur Gear Teeth Defect using YOLOv7 and DeepSort
### Overview
This implementation is the inspection of spur gear teeth defects using state-of-the-art YOLOv7 Object Detection and DeepSort Object Tracking Algorithm. The model was trained on custom collected dataset which are images of 3D printed gears, and the model was trained to achieve over 90% mAP score as application area is intended for product quality inspection. The detection result of the model is then itegrated into DeepSort tracking algorithm to retrieve the track ID of detected objects. The track ID of specific detected label is stored into data structure when the object reach within ROI area, and finally the counts of defect, good and total products are measure based on the numbers of IDs stored in the data structure. The general pipline of the system is as shown in the following figure.
| Detection + Tracking (Counting) |
| :---: |
| 
|
| | |
| 
|
-------------------------------------------------------------
### Contents
* [Inference Speed of _Detection_ and _Detection plus Tracking_](#inference-speed-of-detection-and-detection-plus-tracking)
* [Dataset Preparation](#dataset-preparation)
* [Installation](#installation)
* [Run Demo](#run-demo)
* [Model Training](#model-training)
* [Model Evaluation](#model-evaluation)
* [References](#references)
-------------------------------------------------------------
### Inference Speed of _Detection_ and _Detection plus Tracking_
**System Specification**
* **OS** : Ubuntu 22.04 LTS
* **CPU** : Intel Core i5 6200U 2.30GHz * 4
* **RAM** : DDR4, 4GB
* **GPU** : NVIDIA GeForce 920Mx, 2GB
* **GPU Drvier & CUDA Version** : 515.105.01 | 11.7
| Inferencing | FPS on above System Spec |
| :---: | :---: |
| YOLOv7 (Detection) | 388.6ms |
| YOLOv7 + Deepsort (Detection+Tracking) | 350.3ms |
-----------------------------------------------------------
### Dataset Preparation
The dataset that is being trained on is custom dataset. You can get gear images here-> (link soon), annotate with labelImg as the steps below.
#### Step-1: Data Annotation
* Installing [labelImg](https://github.com/heartexlabs/labelImg) Data Annotation Tool;
```
git clone https://github.com/heartexlabs/labelImg.git # first clone the original Repo on specific directory
conda create -n LabelImg python=3.9.13
conda activate LabelImg
pip install labelimg
```
* Creating pre-defined `classes.txt` file in images(dataset) directory with our interest class-names;
```
Good Teeth
Defect Teeth
```
* Starting Data Annotation for YOLOv7;
```
cd [the cloned LabelImg Repsitory]
python labelImg.py [/path/to/images-datasets] [/path/to/classes.txt]
```
`Make sure YOLO type` > `Create RectBox` > `Save` > `Next`
#### Step-2: Data Splitting
```
python split_dataset.py --folder [annotated-dataset-folder-name] --train [80] --validation [10] --test [10] --dest [/desinated-path-name-dataset]
```
#### Step-3: Making configuration file
* The parameters in [gear_detect.yaml](https://github.com/ThuraTunScibotics/spurgear-teeth-defects-detection-and-tracking-with-YOLOv7-DeepSort/blob/main/data/gear_detect.yaml) are needed to change.
* add the training, validation and testing data path to `train`, `val`, and `test` respectively
* add the numbers of class to class to `nc`, and class names to `names` parameters
* The configuration parameter `nc` number of class in [yolov7-gear-detect.yaml](https://github.com/ThuraTunScibotics/spurgear-teeth-defects-detection-and-tracking-with-YOLOv7-DeepSort/blob/main/cfg/training/yolov7-gear-detect.yaml) is also needed to changed, and we chage to `2` for this case.
----------------------------------------------------
### Installation
Step-1: Install `NVIDIA GPU driver` & `CUDA` if it's not already installed on the machine (in Ubuntu)
```
# Installing the driver with your desired verion number
sudo apt install libnvidia-common-515
sudo apt-get -y install libnvidia-gl-515
sudo apt install nvidia-driver-515-server
# After installing with all above commands, reboot ubuntu
sudo reboot
# After reboot, check the smi table
nvidia-smi
```
Step-2: Creating environment & install dependencies
* Install [anaconda](https://www.anaconda.com/), create conda environment
```
conda create -n spur-gear-teeth-defects python=3.10.6
```
* Install PyTorch & required packages
```
conda activate spur-gear-teeth-defects
pip3 install torch torchvision torchaudio # Recommend: Go to pytorch.org and get the command according to desirements
pip install -r requirements_gpt.txt
```
For training YOLOv7 object detection model and running demo, we need to install [YOLOv7 Weights](https://drive.google.com/file/d/19zlzP6T3aBoR7ZvTXbrWaGquBa9Tx5q0/view?usp=sharing) in the the working directory. If you want to train with other YOLOv7 weights, you can check [here](). To do inferencing with my trained model, download it from [here](https://drive.google.com/file/d/1yfpfoOt8XkpwrSOkvZteVg3_iJoq51Gl/view?usp=share_link), and paste it to the directory `runs/train/yolov7-defect-detect-three2/weights/`
----------------------------------------------
### Run Demo
For running the demo of the project using the trained model, three main cases of demo can be ran including normal defect detection, detection and tracking of the product with counting inspection results, and the results can be saved as CSV file for visualizing inspection charts in Excel. The demo result files are saved in `runs/detect/` path after running each python script.
#### Run for detection
To run detection of spur gear teeth defects, we need to run `detect.py` python script.
* Argument lists;
* `--weights` - path to the trained model, runs/train/.../last.pt
* `--source` - path to inference data source
* `--view-img` - display detecting frame
```
python detect.py --weights runs/train/yolov7-defect-detect-three2/weights/last.pt --source inference/videos/video_6.mp4 --view-img
```
#### Run for detection + tracking -> Counting
To run counting the inspecting result of gear product through `detection` and `tracking`, we need to run the implemented `detect_track_count.py` python script, and the argument are the same as detection.
```
python detect_track_count.py --weights runs/train/yolov7-defect-detect-three2/weights/last.pt --source inference/videos/video_4.mp4 --view-img
```
#### Run for saving inspected data as `CSV` file
For saving the data of inspecting result as CSV file, `--save-csv` is needed to pasre in the command. This saved file is going to save in `./runs/csv/` path, and this csv file is used to visulize the qulity control plot and charts as following figure.
```
python detect_track_count.py --weights runs/train/yolov7-defect-detect-three2/weights/last.pt --source inference/videos/video_4.mp4 --view-img --save-csv
```
### Model Training
To train the model on your machine, make sure YOLOv7 weight file haved already downloaded.
```
python train.py --workers 0 --batch-size 1 --device 0 --data data/gear_detect.yaml --img 640 640 --cfg cfg/training/yolov7-gear-detect.yaml --weights yolov7.pt --name yolov7-defect-detect-three --hyp data/hyp.scratch.custom.yaml --epochs 200
```
------------------------------------------------------------
### Model Evaluation
To evaluate the run model on Tensorboard, run the following;
```
tensorboard --logdir runs/train
```
-------------------------------------------------------
### References