Muhammad K. Kabir

Work place: Department of Computer Science and Engineering, Brac University, Dhaka 1212, Bangladesh

E-mail: muhammad.khubayeeb.kabir@g.bracu.ac.bd

Website:

Research Interests: Deep Learning, Machine Learning

Biography

Muhammad K. Kabir has completed Bachelor degree in Computer Science and Engineering from Brac University, Dhaka, Bangladesh. Currently he is working as a team member in the Multimedia Signal & Image Processing Research Group (MSiP) under the supervision of Dr. Jia Uddin, faculty member of AI and Big Data Department, Endicott College, Woosong University, Daejeon, South Korea. His research area includes- machines learning, deep learning for smart system design, anomaly detection.

Author Articles
Drone Detection from Video Streams Using Image Processing Techniques and YOLOv7

By Muhammad K. Kabir Anika N. Binte Kabir Jahid H. Rony Jia Uddin

DOI: https://doi.org/10.5815/ijigsp.2024.02.07, Pub. Date: 8 Apr. 2024

For ensuring the safety issues, a country should establish a secure monitoring system around the most important places. Due to the huge development in unmanned aerial vehicles (UAV), drone detection is a vital part of the safety monitoring system for reducing threats from neighboring countries or terrorist groups. This paper presents a deep learning-based drone detection method. A You Only Look Once (YOLO) v7 architecture is used to train on the dataset. The training dataset consists of drone images in various environments. The trained model was tested on multiple videos of drones from YouTube. Experimental results demonstrate that the model exhibited a recall of 0.9656 and a precision of 0.9509. In addition, the performance of the model compares with the state-of-art models with YOLOv8, YOLO-NAS, Faster-RCNN architectures and it outperforms the other models by maintaining a more stable precision and recall curve.

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