User details
Rishabh Namdev
@rishabh_namdev_g4q9w105w
Graduate Student
Detroit, MI, United States
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Member since: Jan 21, 2025Experienced Computer Vision Engineer with 3+ years expertise in designing and deploying scalable models for object detection, pose estimation, and image segmentation in real-time applications. Skilled in Python, TensorFlow, and PyTorch, with a proven track record of driving innovation in start-up environments and optimizing systems for operational efficiency.Willing to relocate.
. Developed and deployed computer vision-based systems to analyze complex visual data, including object detection and pose estimation models, achieving production-level accuracy for real-time applications.
• Designed a pose estimation framework to evaluate human motion patterns with a focus on scalability, improving system efficiency by 18% through advanced optimization techniques. . Built and fine-tuned models for object detection and motion analysis using Feature Pyramid Networks (FPN) and EfficientNet, achieving an accuracy improvement of 12% on limited labeled datasets.
• Engineered a real-time motion tracking solution for video analysis using Python and TensorFlow, designed to scale for large datasets in dynamic environments.
• Enhanced model generalization through transfer learning and advanced augmentation techniques, processing sensor data for high-precision performance. . Established and directed robotics-focused STEM labs in over 50 institutions across India, fostering hands-on learning and innovation. . Delivered cutting-edge computer vision solutions that set new benchmarks for real-time performance and scalability in start- up environments.
• Collaborated with senior engineers to design and deploy robust perception algorithms for autonomous systems.
• Assisted in designing perception algorithms for autonomous systems, achieving 95% accuracy in object detection and reducing localization error to 5 cm.
• Contributed to optimizing multi-modal sensor pipelines for real-time data processing, maintaining 99% data integrity at 30 Hz.
• Deployed segmentation models for spatial understanding using TensorFlow and PyTorch, achieving 88% mean Intersection over Union (mloU).
• Supported the implementation of advanced sensor fusion techniques to enhance robotic system reliability in complex environments.