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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. train folders

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

image

Testing Video Demo:

Demo.Pothole.mp4

Just follow☝️ me and Star⭐ my repository

Motivated and supported by the works of,

[Miss. Sakshi Tanwar].