Computer vision lab has been undertaking the following R&D projects from commissions and as self-initiatives.
Detection of Chemicals Using Nano-material Based Sensor Measurements: Chemical (gas) detector based on a single sensor are being developed indigenously using nano-material. This detector has a higher false positive rate than the desired value. It is desired to reduce the false positive rate using AI techniques and include more gases in the repository for detection.
Urdu Hand-written Words Recognition using Deep Learning Techniques: In computer vision and pattern recognition domains, Urdu handwritten words recognition is one of the most complicated and challenging tasks such as accessibility of various handwriting styles, the shape comparability of distinct characters, and the cursive nature of the text. Computer Vision Lab has undertaken this self-initiative project to implement DL techniques for Urdu hand-written recognition words, because Urdu language is broadly spoken and written in the territory of South-East Asia such as India, Pakistan, Bangladesh, and Afghanistan. This has many applications such as document digitization, human-computer interaction, paper examination, online accessibility of old work, automating official duties, data entry forms, signature authentication, postal address interpretation, bank receipts, automated transcription, preservation of cultural heritage, and historical text analysis. The project will initial focus on around 100 Urdu handwritten words. Literature survey, data collection, annotation & labeling, implementation has been completed.
Road Pavement Condition Monitoring: Main objective of this self-initiative project is to devise an AI-based pavement condition assessment solution for repair and resources management through accurate detection and efficient analysis, thus ensuring safe and smooth flow of traffic. For this purpose, we will develop a deep learning based approach to figure out the abnormalities in road surfaces. This project is composed of Object detection, Localization (locates objects in image/frame), Classification (predicts category/class of object), Object Counting Via tracking and Report generation.
Generating Multi-domain Deep Fake Images using Latent Transformations of Explainable AI: The main objective of this self-initiative research project is to generate CFs explanation using the latent transformations technique. Latent transformation utilize multi-domain mapping to transform image feature and enhance generated image quality using different architecture variations in latent transformations. Major applications of this research include Synthetic data generation, Image-to-image translation, Art work and security (deep fake detection).
Facial Recognition based Entry/Exit Logger / System: The main objective of this project is to develop a touch-free biometric attendance system based on facial recognition with a high accuracy, for AITeC building in NCP.
Long Range Object of Interest Detection in Visible/IR Videos: This project focus to use an unmanned aerial vehicle (UAV) with a payload of color / greyscale and IR (thermal) cameras for aerial video(s). The payload cameras can operate for video acquisition at a slant range of up to 15 Km under different zoom levels. Currently, a human operator has to sit for hours to manually detect humans from long range visible and IR videos collected from payload cameras aboard a UAV. The project aims to develop an automated solution using AI techniques for this purpose to enhances the system capability.
Conversion of RGB Images to Thermal Images: This project has an objective to generate a Thermal image as output of an AI model from in RGB input image to the model.