https://github.com/Rushi314/GPR-Object-Detection

This repository contains code to train object detection models like FRCNN/YOLO for identifying objects in Ground Penetrating Radar scans. It also contains code to generate fake data using Generative Adversarial Networks(GANs).
https://github.com/Rushi314/GPR-Object-Detection

frcnn gans generative-adversarial-network ground-penetrating-radar machine-learning mmdetection object-detection yolo

Last synced: 16 days ago
JSON representation

This repository contains code to train object detection models like FRCNN/YOLO for identifying objects in Ground Penetrating Radar scans. It also contains code to generate fake data using Generative Adversarial Networks(GANs).

• Host: GitHub
• URL: https://github.com/Rushi314/GPR-Object-Detection
• Owner: Rushi314
• Created: 2021-06-10T15:03:14.000Z (almost 4 years ago)
• Default Branch: main
• Last Pushed: 2022-02-01T22:37:15.000Z (over 3 years ago)
• Last Synced: 2024-11-09T17:42:52.343Z (6 months ago)
• Topics: frcnn, gans, generative-adversarial-network, ground-penetrating-radar, machine-learning, mmdetection, object-detection, yolo
• Language: Jupyter Notebook
• Homepage:
• Size: 12.6 MB
• Stars: 31
• Watchers: 1
• Forks: 8
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-yolo-object-detection - Rushi314/GPR-Object-Detection - Object-Detection?style=social"/> : Detecting Objects in Ground Penetrating Radars Scans. (Object Detection Applications)
• awesome-yolo-object-detection - Rushi314/GPR-Object-Detection - Object-Detection?style=social"/> : Detecting Objects in Ground Penetrating Radars Scans. (Applications)

README

        
# Detecting Objects in Ground Penetrating Radars Scans

We used mmdetection library for fine-tuining Faster RCNN, Mask RCNN, and YOLO to 

identify objects from Ground penetrating radar scans.

### Data: 

- Used publicaly available dataset containing 171 annotated 

GPR scans from https://github.com/irenexychen/gpr-data-classifier.

- Converted the annotations from .xml format to .coco format using 

https://github.com/yukkyo/voc2coco.

### Creating fake data using GANS: 

- Code: GPR_GAN_071521_v.0.4.ipynb

#### GANs architecture(created from scratch):

- Generator: 

  - Input: A tensor of size (100, 1, 1) filled with random numbers from a normal distribution(mean = 0, variance = 1)

  - Output: A tensor of size (3, 128 , 128) representing Image

  - NN Architecture: 6 transpose convolutions with batch normalization and ReLU activations. 

- Discriminator:

  - Input: (3, 128, 128) size tensor either real or fake

  - Output: Classification of input as either real(1) or fake(0)

  7 Conv layers with batch normalization and ReLU

- Loss: Binary Cross Entropy Loss

#### GANs results:

![](GAN_results.png)

### Hyperbola detection:

- Code: GPR_MMDetection_v0.022.ipynb

- We finetuned multiple object detection models with various checkpoints and due to the scarsity of GPR scans, 

the FRCNN model pretrained on MS-COCO dataset with resnet-101

as backbone gave the best results.

### Results: 

- We finetuned FRCNN with both real images and fake images from GANs

- Achieved mAP 0.90 in detecting hyperbolas 

![](FRCNN_RESULTS.png)