Karishma Pawar

Papers

Total Citations

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About

Karishma Pawar is a researcher whose work lies at the dynamic intersection of computer vision and deep learning, with a particular focus on advancing object detection methodologies. Her most-cited paper, "Assessment of Object Detection Using Deep Convolutional Neural Networks" (2018, 25 citations), provides a critical evaluation of how deep convolutional neural networks (CNNs) can be optimized for real-world detection tasks. In this work, Pawar systematically benchmarks various CNN architectures, identifying key trade-offs between accuracy and computational efficiency—a contribution that has guided subsequent studies in autonomous systems and surveillance. Her research has been cited by engineers and academics seeking to deploy robust detection models in resource-constrained environments. Though early in her career, Pawar’s assessment framework has already influenced practical implementations in fields ranging from robotics to medical imaging. By demystifying the performance of deep learning models, she has helped bridge the gap between theoretical advances and applied computer vision, making her a promising voice in the ongoing evolution of intelligent visual systems.