I am a recently graduated computer vision enthusiast and working currently as an algorithm developer in computer vision for autonomous driving. Check out my Master Thesis, where I have presented my thesis on road damage detection based on GPR and Deep learning models. It utilises TensorFlow Object Detection API and I have provided an easy tutorial to train a custom object detecotr on TensorFlow.

Currently, I am working with CenterNet, Yolov3 and Yolov5 on PyTorch on the same topic and on vechile and pedestrain detections. PyTorch provides great speed in running the inferences and hence my current project is to run the inference on an autonomous car to detect objects based on Camera and road damages based on GPR sensors.

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This project represents the topic of my master thesis. It aims to implement object detection through deep CNNs for road subsurface damage and feature detection from groundpenetrating radar (GPR) road measurements. By integrating DL algorithms and GPR technique, this work focuseson autonomous, faster and efficient detection of road damages and features with the idea to minimize the human intervention. Here, [TensorFlow Object Detection API] (https://github.com/tensorflow/models) is implemnted and a Deep CNN model is trained on a custom GPR dataset.

Keywords — Object Detection, Computer Vision, Deep Learning, TensorFlow, Faster RCNN
Tools — Python, TensorFlow, OpenCV, Pandas, Numpy, GPR tools

Repository

The projects explores the object detection application for autonomous driving. It a PyTorch implementation of YOLOv3 object detection model based on this: [Yolov3 - Ultralytics](https://github.com/ultralytics/yolov3). Th edetection is model is customized for 16 bit GPR B-scans and RGB-D images on CARLA.

Keywords — Object Detection, Computer Vision, Deep Learning, PyTorch, YOLOv3, Autonomous Vehicle
Tools — Python, PyTorch, OpenCV, Pandas, Numpy, MATLAB

Repository