Inferigence Quotient

Machine Learning Enginner (Computer Vision), Ex-Intern

April '22 - Present

As a Machine Learning Engineer at Inferigence Quotient, I led the development and deployment of cutting-edge computer vision solutions, focusing on real-time object detection and recognition systems. My work involved spearheading a cross-functional team in the creation of an Automatic Number Plate Recognition (ANPR) system, which significantly improved detection accuracy and reduced manual tracking errors across multiple deployment sites. I also engineered a high-performance object recognition pipeline for UAVs, which enhanced operational efficiency in surveillance missions by 30% and reduced processing time by 50%. Additionally, I developed a real-time georeferencing system for UAV-captured images, achieving 95% accuracy in aligning images with satellite imagery, thereby improving geolocation accuracy by 40%. My contributions not only advanced the technical capabilities of our systems but also directly impacted client operations by providing reliable, scalable solutions.

Primary Tasks and Responsibilities:

1. Automatic Number Plate Recognition - ANPR

• Directed a cross-functional team of 8 data engineers and ML engineers in the deployment of a scalable number plate tracking solution, incorporating custom OCR correction logic specifically tailored for Indian number plates.
• Orchestrated the development and integration of advanced detection algorithms that elevated detection accuracy to over 95%, significantly enhancing data integrity; this initiative led to a reduction in error rates to under 2% and greatly improved service reliability for law enforcement and civilian applications.
• Successfully deployed the software solutions at 10 toll plazas, resulting in a 40% reduction in manual tracking errors and empowering law enforcement agencies to respond to traffic violations in real-time, significantly improving operational efficiency.

2. System for Tracking And Recognition of Targets - iSTART

• Engineered a robust object recognition and tracking pipeline for deployment within Unmanned Aerial Vehicles (UAVs), implementing state-of-the-art tracking algorithms such as DeepSort alongside custom-trained YOLOv7 models, optimized for real-time performance.
• Utilized frameworks and libraries including OpenCV, TensorRT, and onnxruntime in C++ to design and develop the system, ensuring it was fully optimized for deployment on Jetson devices, thus achieving high efficiency and low latency in aerial surveillance applications.
• Led the development of an in-house image annotation tool and oversaw the curation of custom datasets tailored for deep learning projects, managing and mentoring a team of 3 members to ensure the successful delivery of high-quality data for model training.

3. Real-time Georeferencing of Aerial Infrared (IR) Video - GeoAIR

• Leveraged Python and OpenCV to develop a precise frame registration pipeline, aligning UAV-captured infrared images with satellite imagery to achieve accurate geo-location for real-time aerial surveillance applications.
• Incorporated advanced template matching algorithms combined with sparse and dense optic flow techniques, achieving close to 90% frame registration accuracy and delivering performance on HD videos with latency under 500ms and throughput of 25fps on a moderate capacity GPU, meeting stringent real-time operational requirements.

Visual Computing Lab, Indian Institute of Science (IISc)

Research Intern (Deep Learning)

May '21 - April '22

Former Research Intern under Professor Anirban Chakraborty at the Visual Computing Laboratory, operated by the Department of Computational Data Sciences, at the Indian Institute of Sciences (IISc). As a Research Intern at the Visual Computing Lab, IISc, I contributed to the development of advanced deep learning models for domain adaptation and image inpainting. I collaborated on integrating the Divide-Mix algorithm into the Source-Free Multi-Label Domain Adaptation (SF-MLDA) framework, which led to a 7% increase in model accuracy by mitigating data noise. Additionally, I worked on developing neural networks that utilized Generative Adversarial Networks (GANs) and Autoencoders, resulting in a 30% improvement in anomaly detection efficiency. My research focused on advancing the state-of-the-art in these areas, with outcomes that were not only academically significant but also had practical implications for improving the reliability and robustness of machine learning models.

Primary Tasks and Responsibilities:

1. Source Free Multi-Label Domain Adaptation - SF-MLDA

• Played a pivotal role in the development of an advanced framework for performing Source-Free Multi-Label Domain Adaptation (SF-MLDA), leveraging cutting-edge techniques to address domain shift challenges in machine learning.
• Spearheaded the integration of a co-teaching algorithm called Divide-Mix to effectively mitigate noise in training data within the SF-MLDA framework, resulting in a measurable 7.0% improvement in model accuracy and robustness.

2. Superpixel Masking and Image Inpainting - SMAI

• Led the development and optimization of two neural networks inspired by Generative Adversarial Networks (GANs) and Autoencoders for advanced anomaly detection, localization, and correction, significantly improving the system’s effectiveness.
• Conducted in-depth experiments with structural and reconstruction loss functions to establish a strong correlation between image inpainting quality and reconstruction accuracy, contributing to the refinement of the overall pipeline.
• Enhanced the anomaly detection pipeline by incorporating multi-exposure fusion techniques for synthetic image regeneration, achieving a remarkable 80.0% overall accuracy rate, setting a new standard for image inpainting applications.