AV-Dataverse-Hack-Build-an-AI-Model-to-Save-Lives

Competition hosted on Analyticsvidhya

About

Build a computer vision-based technology to process and detect the potholes present in an image.

This is my first object detection - computer vision hackathon.

From the competition starter notebook, I have tried the same PyTorch - fasterrcnn resnet50 fpn object detection model, and without the proper image processing or data augmentation the model not performed well and didn't learn any signals.

Final Competition score is 0.0060050745

Leaderboard Rank is 72/77

Evaluation Metric is mAP@[.5,.95]

Post-competition I personally tried the yolo object detection model with wandb logging. The model scored 38%(100*mAP[.5,.95]) on test dataset.

Wandb Model logging

Model demo Road Pothole Detection Using YOLOv7

File information

• av-dataverse-hack-build-an-ai-model-to-save-lives- EDA.ipynb

  Basic Exploratory Data Analysis

  Packages Used,

   * seaborn
   * Pandas
   * Numpy
   * Matplotlib
   * PIL
   * cv2
   * os
   * distance
   * imagehash
   * time
   * itertools
  

  Extract basic information about the images(width, height, color mode) and analyzed the information through visualization in the following methods.

  Total pothole wise image samples.

  

  RGB color distribution analysis.

  

  Find the similar images using different hashing algorithms.

    * Average hashing - Total matched images 284
    * Perceptual hashing - Total matched images 80
    * Difference hashing - Total matched images 80
    * Wavelet hashing - Total matched images 280
    * Color hashing - Total matched images 217120
  

  From the above various image hashing algorithm, the perceptual hashing, and difference hashing algorithms significantly find similar images based on the hash value.