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:

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