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About

Sweta Panigrahi is a researcher at the forefront of computer vision, specializing in pedestrian detection—a critical component for autonomous vehicles, surveillance, and robotics. Her work focuses on enhancing the accuracy and efficiency of deep learning models, particularly by refining the YOLO (You Only Look Once) framework. In her highly cited 2022 paper, "InceptionDepth-wiseYOLOv2," she introduced an improved implementation that balances detection speed and precision, garnering 15 citations. That same year, her "MS-ML-SNYOLOv3" proposed a robust, lightweight modification of SqueezeNet-based YOLOv3, achieving 14 citations for its resource-efficient design. Earlier, in 2021, Panigrahi advanced the field by fusing hand-crafted features with a multi-layer ResNet model, demonstrating that traditional feature extraction still holds value when integrated with modern architectures. Her contributions have been recognized as among the most sought-after in object detection, directly impacting real-world applications where reliable pedestrian identification is paramount. With a growing citation record and a focus on practical, deployable solutions, Panigrahi is establishing herself as a key innovator in making AI-driven detection systems both smarter and more accessible.