Shikha Bhalla

About

Shikha Bhalla is making waves in the field of computer vision, with a sharp focus on the challenging domain of underwater object detection. Her research addresses the critical need for robust and efficient solutions in marine exploration, environmental monitoring, and underwater robotics. Bhalla’s major contributions lie in developing advanced deep learning architectures that overcome the unique visual distortions of underwater environments. Her most cited work, "Feature-adaptive FPN with multiscale context integration for underwater object detection" (2024), has already garnered 14 citations, showcasing its immediate impact. She further advanced the field with "HydR-CNN," a multi-stage framework that innovatively combines a Hybrid R-CNN with a Pyramid Vision Transformer and augmented convolution. Demonstrating a comprehensive approach, Bhalla also introduced a novel underwater marine dataset featuring diverse scenarios, providing a critical benchmark for the research community. Her work is rapidly establishing her as a key innovator in making autonomous underwater perception more accurate and reliable.

Research Focus

Key Achievements

Top Papers

1. 1
   Feature-adaptive FPN with multiscale context integration for underwater object detection

   14 citations · 2024
2. 2
   HydR-CNN: advancing underwater object detection using a multi-stage framework with hybrid R-CNN and pyramid vision transformer with augmented convolution

   3 citations · 2025
3. 3
   A Novel Underwater Marine Dataset with Diverse Scenarios for Robust Object Detection

   2 citations · 2024

Key Collaborators

Contact & Links