Muhamad Amirul Haq
Papers
2
Total Citations
26
H-Index
2
About
Muhamad Amirul Haq is a rising researcher in the field of autonomous driving and computer vision, with a focused expertise in monocular 3D object detection. His work addresses a critical challenge in the industry: enabling low-cost, robust 3D perception using only a single camera, without the expense of lidar or stereo systems. In his highly cited 2021 paper, "3D Object Detection Based on Proposal Generation Network Utilizing Monocular Images" (16 citations), Haq pioneered a proposal generation network that significantly improves detection accuracy from monocular inputs. He further advanced this line of research in 2023 with "Monocular 3D Object Detection Utilizing Auxiliary Learning With Deformable Convolution" (10 citations), introducing deformable convolution and auxiliary learning techniques to enhance feature extraction and model robustness. Together, these works have accumulated over 26 citations, reflecting their growing influence in the autonomous vehicle community. Haq’s contributions are particularly notable for their practical impact, offering a scalable and cost-effective solution for safety-critical detection systems. His research continues to drive progress toward more accessible and reliable autonomous driving technologies.
Research Focus
Key Achievements
Top Papers
- 1
- 2