Robust Deep Learning Empowered Real Time Object Detection for Unmanned Aerial Vehicles based Surveillance Applications
- Title
- Robust Deep Learning Empowered Real Time Object Detection for Unmanned Aerial Vehicles based Surveillance Applications
- Creator
- Ranjith C.P.; Hardas B.M.; Khaja Mohideen M.S.; Nijil Raj N.; Robert N.R.; Mohan P.
- Description
- Surveillance is a major stream of research in the field of Unmanned Aerial Vehicles (UAV), which focuses on the observation of a person, group of people, buildings, infrastructure, etc. With the integration of real time images and video processing approaches such as machine learning, deep learning, and computer vision, the UAV possesses several advantages such as enhanced safety, cheap, rapid response, and effective coverage facility. In this aspect, this study designs robust deep learning based real time object detection (RDL-RTOD) technique for UAV surveillance applications. The proposed RDL-RTOD technique encompasses a two-stage process namely object detection and objects classification. For detecting objects, YOLO-v2 with ResNet-152 technique is used and generates a bounding box for every object. In addition, the classification of detected objects takes place using optimal kernel extreme learning machine (OKELM). In addition, fruit fly optimization (FFO) algorithm is applied for tuning the weight parameter of the KELM model and thereby boosts the classification performance. A series of simulations were carried out on the benchmark dataset and the results are examined under various aspects. The experimental results highlighted the supremacy of the RDL-RTOD technique over the recent approaches in terms of several performance measures. 2022 River Publishers.
- Source
- Journal of Mobile Multimedia, Vol-19, No. 2, pp. 451-476.
- Date
- 2023-01-01
- Publisher
- River Publishers
- Subject
- computer vision; deep learning; image processing; object detection; Surveillance; unmanned aerial vehicles
- Coverage
- Ranjith C.P., Faculty in Information Technology Department, University of Technology and Applied Sciences, Shinas, Oman; Hardas B.M., Electronics Engineirng Dept, Shri Ramdeobaba College of Enginering and Management, Nagpur, India; Khaja Mohideen M.S., Department of Information Technology, University of Technology and Applied Science, Salalah, Oman; Nijil Raj N., Department of Computer Science and Engineering Younus College of Engineering and Technology, Kollam, India; Robert N.R., Department of Computer Science, Christ University, Bangalore, India; Mohan P., School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India
- Rights
- All Open Access; Gold Open Access
- Relation
- ISSN: 15504646
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Ranjith C.P.; Hardas B.M.; Khaja Mohideen M.S.; Nijil Raj N.; Robert N.R.; Mohan P., “Robust Deep Learning Empowered Real Time Object Detection for Unmanned Aerial Vehicles based Surveillance Applications,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 26, 2025, https://archives.christuniversity.in/items/show/14671.