FReD: Full Recognition for architectural floor-plan Drawings using deep learning

Designed , built and deployed into production, an End-to-End solution for floor-plan recognition capable to recognize and reproduce all visual aspects and object of a raster floorplan into SVG. Leveraging the spatial complexity in object detection by using oriented detection and rotation equivarient neural networks. ( see thesis abstract in the corresponding section, contact for the full thesis )

• Floor-plan pre-processing : CAD data to raster high quality conversion.

• Data annotation, pre-processing and augmentation for the training.

• Training and deploying state-of-the-art object detection models.

• Built an extensive comparative study of 10+ state-of-the-art Horizontal and Oriented Object Detection.

Visual e-reputation quantifier

A hybrid approach to quantify brands visual presence compared to its competitors on social media and news websites:
  • Built a Faster R-CNN object detection model with a COCO ResNet101 Transfer Learning backbone to detect logos from input images. This model have been trained on GCP with 4 NVIDIA Tesla k80 GPUs.
  • Converted the model weights to TensorRT in order to accelerate the inference when performed on GPU.
  • The model predictions are stored in a database on which we applied the Digital Share of Voice metric transformation.
  • Built a Dashboard that monitors the results and gets refreshed automatically with each bulk inference done by the model.
Used tools, libraries and techniques : Python, OpenCV, TensorFlow, Keras, TensorRT, Faster R-CNN, GCP, Transfer Learning, Microsft PowerBI.

Smoke Auto Labeler

This project to general object detection labels for smoke images:
  • Developed to help Pyronear automatically annotate wildfire images from the web in order to expand the current dataset.
  • Uses a pretrained SSD Mobilenet V2 model to make bounding boxes prediction then automatically generate them into XML labels which is the PASCAL VOC 2007 label type
  • Model trainined on the wildfire Smoke Dataset released by AI For Mankind with Tensorflow

Landmark Detection and Robot Tracking (SLAM)

This project is an implementation of the SLAM (Simultaneous Localization

and Mapping) for a 2 dimensional world: