Muhammad Iqbal
Papers
1
Total Citations
3
H-Index
1
About
Muhammad Iqbal is a rising researcher in computer vision and applied machine learning, with a focus on practical, accessible object detection systems. His work bridges the gap between cutting-edge deep learning models and real-world usability, particularly through comparative evaluations of lightweight architectures. In his most-cited study, "Effectiveness of Teachable Machine, MobileNet, and YOLO for object detection: A comparative study on practical applications," Iqbal systematically assessed three popular detection frameworks—Google's Teachable Machine, MobileNet, and YOLO—using a custom dataset spanning four everyday categories: bird, horse, laptop, and sandwich. This work, published in 2025 and already garnering 3 citations, provides critical insights into model efficiency, accuracy, and deployment feasibility for non-expert users. By highlighting the trade-offs between computational cost and detection performance, Iqbal's research informs the development of user-friendly AI tools for education, hobbyists, and small-scale industrial applications. His contributions are particularly valuable for students and practitioners seeking to understand which object detection approach best suits resource-constrained environments. As his citation count grows, Iqbal is establishing himself as a thoughtful voice in democratizing computer vision technology.
Research Focus
Key Achievements
Top Papers
- 1