Mominul Ahsan
Papers
1
Total Citations
5
H-Index
1
About
Mominul Ahsan is a researcher advancing the frontiers of lightweight, real-time computer vision for robotic applications. His primary focus lies in developing efficient deep learning architectures that enable intelligent automation, particularly in the domain of robotic waste sorting—a critical area for sustainable manufacturing and environmental robotics. His most notable contribution, "RTDRNet-lite: A lightweight real-time detection framework for robotic waste sorting" (2025), introduces a streamlined neural network designed to balance high detection accuracy with minimal computational overhead, making it viable for deployment on resource-constrained robotic platforms. This work addresses a pressing need for scalable, real-time object recognition in dynamic sorting environments. While his citation count is still growing, with the 2025 paper already garnering 5 citations, the work signals strong early impact and relevance in the intersection of robotics, computer vision, and circular economy. Ahsan’s research is particularly valuable for students and engineers seeking practical, deployable solutions for automated waste management, and his approach exemplifies how lightweight models can democratize advanced robotics for real-world environmental challenges.
Research Focus
Key Achievements
Top Papers
- 1