Zhengzhou University, China
Usman Ahmad is a dedicated researcher and data scientist specializing in computer vision and deep learning. Currently pursuing a Ph.D. in Electrical and Information Engineering at Zhengzhou University, China, his research focuses on small aerial object detection using advanced deep learning models. With a Master’s degree in Electrical and Computer Engineering from South China University of Technology, Usman has developed expertise in CNN-based neural networks for small object detection, a critical area in applications like autonomous driving and remote sensing. His professional journey includes roles as a freelance data scientist, site engineer, and visiting faculty member, showcasing his versatility in both academia and industry. Usman’s work has been published in prestigious journals like IEEE Geoscience and Remote Sensing Letters, reflecting his contributions to the field. Passionate about innovation, he continues to push the boundaries of object detection technologies.
Professional Profile
Google Scholar
Education 🎓
Usman Ahmad holds a Ph.D. in Electrical and Information Engineering from Zhengzhou University, China (2022–present), focusing on small aerial object detection through deep learning. He earned his M.S. in Electrical and Computer Engineering from South China University of Technology (2018–2020), where he specialized in small object detection using CNN-based networks, achieving a final grade of 3.4/4. His thesis, Small Object Detection Through CNN-Based Network, addressed challenges in detecting small-scale images in high-resolution and remote sensing applications. Earlier, he completed his B.S. in Electrical Engineering from the National University of Computer and Emerging Sciences (FAST-NUCES), Islamabad (2010–2014), with a thesis on Prepaid Energy Meter, which tackled issues like power theft and billing inefficiencies. His academic journey reflects a strong foundation in electrical engineering and a progressive shift toward cutting-edge AI and computer vision research.
Experience 💼
Usman Ahmad has a diverse professional background. As a Freelance Data Scientist (2020–present), he developed CNN-based models for small image detection and OpenCV machine learning algorithms with 82% efficiency. From 2015 to 2018, he served as a Site Engineer at China State Construction Engineering Corporation Ltd, contributing to the New Islamabad International Airport Project, where he managed MEP systems and HVAC electrical parameters. He also worked as a Visiting Faculty Member at the University College of Engineering & Technology, Sargodha (2014–2015), teaching electrical and computer engineering while managing labs and exams. His technical expertise spans deep learning, computer vision, and electrical systems, making him a versatile professional in both academic and industrial settings. His hands-on experience in large-scale construction projects and AI-driven solutions highlights his adaptability and problem-solving skills.
Awards and Honors �
Usman Ahmad’s contributions to the field of computer vision and deep learning have earned him recognition in prestigious journals. His research on infrared small target detection, published in IEEE Geoscience and Remote Sensing Letters (2022), has been cited 42 times, showcasing its impact. He co-authored MCDNet: An Infrared Small Target Detection Network Using Multi-Criteria Decision and Adaptive Labeling Strategy (2024), published in IEEE Transactions on Geoscience and Remote Sensing, which has already garnered citations. His innovative work on GAN-integrated feature pyramid networks for small aerial object detection further underscores his expertise. While specific awards are not listed, his consistent publication record in high-impact journals and active contributions to advancing object detection technologies highlight his academic excellence and dedication to the field.
Research Focus 🔍
Usman Ahmad’s research centers on small object detection using deep learning models, particularly in high-resolution and remote sensing applications. His work addresses the challenges of detecting small-scale objects, such as distant vehicles in autonomous driving or tiny structures in satellite imagery. He has developed advanced CNN-based networks and GAN-integrated feature pyramid networks to improve detection accuracy and speed. His research also explores infrared small target detection, employing innovative strategies like adaptive labeling and multi-criteria decision-making. By focusing on practical applications like self-driving cars, remote sensing, and surveillance, Usman aims to bridge the gap between theoretical advancements and real-world solutions. His contributions have been published in leading journals, reflecting his commitment to pushing the boundaries of computer vision and AI.
Publication Top Notes📚
- Infrared small target detection network with generate label and feature mapping – IEEE Geoscience and Remote Sensing Letters (2022)
- MCDNet: An Infrared Small Target Detection Network Using Multi-Criteria Decision and Adaptive Labeling Strategy – IEEE Transactions on Geoscience and Remote Sensing (2024)
- Small Aerial Object Detection through GAN-Integrated Feature Pyramid Networks – In Progress
Conclusion 🌟
Usman Ahmad is a highly skilled researcher and data scientist with a strong background in electrical engineering and deep learning. His expertise in small object detection, particularly through CNN-based and GAN-integrated models, has made significant contributions to computer vision and remote sensing. With a robust academic foundation, diverse professional experience, and a growing list of impactful publications, Usman continues to drive innovation in AI and its real-world applications. His dedication to solving complex problems and advancing technology makes him a valuable asset to the field.