Our Customers

 

Computer Vision based on Deep Learning

3D object recognition

For a Building Information Management company

Business Problem :

The current state of construction site progress measurement is manual. Building information management (BIM) system implementation provider wanted to integrate object recognition with BIM. This will enable BIM to track site construction progress, it will also detect any anomalies in the site construction.

Technology Used :

Python, OpenCV, Tensorflow, Deep Learning using PointNet++

Solution :

The solution is developed using Pointnet++, which is a pre-built deep learning network for classification & segmentation.  Pointnet++ is pre-trained on Shapenet & Modelnet, hence provide transfer learning capability for new data.  The customer has provided point cloud datasets for small, medium & large objects captured using LIDAR primarily for pump, bend & valve. This data is then processed using python point cloud library (pcl) and uniformly sampled to make it ready for Pointnet++. The model recognized these objects with more than 90% accuracy.

Scapula & Humerus Bone Detection in DiCOM images

A Senior Researcher @ Top NA University

Business Problem :

The researcher is working on a research project related to the shoulder bones i.e. Scapula, Humerus & Implants. The input data is the 3D DiCOM images of Shoulder i.e. the X-Ray, CT Scan files. The business need was to identify these two bones (Scapula & Humerus) from the DiCOM files accurately. The first phase is 2D object detection and improvise the solution to 3D object detection.

Technology Used :

Python, OpenCV, Tensorflow, Deep Learning using R-CNN & Mask CNN, U-Net

Solution :

The 3D DICOM images were converted into 2D DICOM images. The object labelling was a key task, some of the images had the bones appearing in the image from more than one position. So the labelled bounding boxes were overlapping, and thus detecting the overlapping object was a challenge. We evaluated the deep learning algorithms like R-CNN, Mask R-CNN & U-Net. Both Mask R-CNN & U-Net gave an object detection accuracy of over 90%.

Face Recognition & Object Detection

Product company serving Japanese market

Business Problem :

The customer wanted to automate billing in their cafeteria based on the food served. The requirement was to detect person using face recognition and food ordered using plate/bowl types.

Technology Used :

Python, OpenCV, Tensorflow, Deep Learning

Solution :

The deep learning based solution for object detection (plate/bowl) is developed using Python & Tensorflow. The deep learning model is trained on training data set provided by customer. These models are integrated with restaurant billing software of our client. Evaluated model performance on high end cpu and gpu machine on cost, speed and accuracy parameters. Achieved face recognition and object detection speed in less than 1 second with more than 90% accuracy.

Smart City – Waste Classification

A municipality of an European Country

Business Problem :

The municipality has various kind of Waste Containers which are hosted on roads and localities. The waste could be Glass, Plastic, Liquid, Food Remains, Furniture etc. People dump the waste in the Waste Bin and the bigger waste like Furniture, Mattresses etc are dumped near the Waste Container. For each type of waste,  different departments of Municipality as responsible. Citizens can report about the garbage lying near the container by taking the pictures and uploading it on the Smart city app. The business problem was to detect various categories of waste from the upload image and return back the appropriate department to the App.

Technology Used :

Python, TensorFlow, OpenCV, Deep Learning, NLP

Solution :

STY developed custom algorithms to process the text & images uploaded by the citizens on the Mobile App. The algorithms were trained on the training images provided by the Customer. Based on the various type of wastes, the algorithm would return the right department. Along with image, the citizens would also write about the issue. Using NLP based Customer algorithms, we could classify the text, identify the correct department and return it to the App. The accuracy achieved was over 94% which is a great accuracy to have.