Papers
4
Total Citations
34
H-Index
3
About
Riti Kushwaha is a researcher at the forefront of computer vision and autonomous systems, with a primary focus on object detection in challenging, real-world environments. Her work spans two critical domains: aerial surveillance and underwater robotics. In her highly cited 2019 paper (15 citations), Kushwaha pioneered a method for person identification using autonomous drones and resource-constrained devices, addressing the urgent need for efficient missing-person search in crowded areas by combining advanced algorithms with smart hardware. More recently, she has made significant contributions to underwater object detection, a field vital for marine exploration and environmental monitoring. She introduced the novel "HydR-CNN" framework, a multi-stage architecture that fuses Hybrid R-CNN with a Pyramid Vision Transformer and augmented convolution, achieving state-of-the-art results. Complementing this, she developed a diverse, novel underwater marine dataset (2024) specifically designed to benchmark deep learning methods under varied and difficult conditions. With her feature-adaptive FPN and multiscale context integration work (14 citations), Kushwaha is establishing herself as a key innovator in making object detection robust across both aerial and aquatic domains.
Research Focus
Key Achievements
Top Papers
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