pothole_video.mp4

POT-HOLE-DETECTION-YOLOV5

Overview :

The purpose of this project is to create a Deep Learning model and this dataset could be used for automatically finding and categorizing potholes in city streets so the worst ones can be fixed faster.

📁 Dataset Used :

https://public.roboflow.com/object-detection/pothole

This dataset consist of only one class: (Pothole)

Workflow:

Data Preparation:

• Total 465 images for training and 133 images for validation present in 2 classes.
• Create a bounding boxes with the help of label-img And makesense.ai website according to YoloV5.
• Prepare folder structure that can be accept by YoloV5.

Steps to use Yolov5:

• Cloning the YoloV5 file from official repository.
• Changing the directory of yolov5
• Installing the dependencies
• Download all versions pre-trained weights.

Steps Before Training Custom Dataset:

• Go to yolov5/data/.
• Open data.yaml
• Edit the following inside it:

1. Training and Validation file path
2. Number of classes and Class names.

Training YOLOV5 Model

• Set images size 640 with batch of 8.
• Train model around 600 epochs .
• Visualise the training metrics with the help of tensorboard.

Testing Images Using Test Data

Testing Video Demo:

Demo.Pothole.mp4

Just follow☝️ me and Star⭐ my repository

Motivated and supported by the works of,

[Miss. Sakshi Tanwar].