Super pixels transmission map-based object detection using deep neural network in UAV video

ABSTRACT

Object detection has become a very prominent subject for research in recent times. This study's main goal is to suggest a technique for video saliency object detection. It seems to sense that using the depth information in photos to detect salient things. Since depth offers abundant information about scene structure, object forms, and other 3D cues. This information is very compatible to distinguish between objects in the foreground and background. As a result of the high object density, small object size, and cluttered background, aerial photos and movies provide results with low precision. In this paper, the proposed SPTM (Super Pixel Transmission Map)-YOLO model, the input RGB image has applied Dark Channel Prior (DCP) method for estimating the transmission map. From the transmission map only, the background probability is estimated with the help of SLIC (simple linear iterative clustering algorithm) superpixel segmentation. That foreground extracted image is further learned with YOLO architecture to detect the objects effectively. For object detection in aerial images, this proposed SPTM-YOLO approach outperforms classic YOLO by up to 6% accuracy. Accurate detection of things that are small in size, partially occluded, and out of view is possible.

KEYWORDS:

• Dark channel prior
• YOLO
• object detection
• aerial images
• transmission map
• deep learning
• superpixel

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

J. Evangelin Deva Sheela

Evangelin Deva Sheela J. graduated B.Sc. in Computer Science from Sarah Tucker College, Perumalpuram, Tirunelveli, Tamil Nadu, India in 2011. Then she received her Master of Computer Applications from Francis Xavier Engineering College, Tirunelveli, Tamil Nadu, India in 2014. Later, she received her M.Phil. degree in Computer Science from VPMM College for Women, Krishnan Kovil street, Srivilliputhur, Tamil Nadu, India. She is pursuing her Ph.D. in Manonmaniam Sundaranar University. Her research interests include image and Video Processing.

P. Arockia Jansi Rani

Dr. Arockia Jansi Rani P. graduated B.E. in Electronics and Communication Engineering from Government College of Engineering, Tirunelveli, Tamil Nadu, India in 1996 and M.E. in Computer Science and Engineering from National Engineering College, Kovilpatti, Tamil Nadu, India in 2002. She has been with the Department of Computer Science and Engineering, Manonmaniam Sundaranar University as Assistant Professor since 2003. She has more than 14 years of teaching and 11 years of research experience. She completed her Ph.D. in Computer Science and Engineering from Manonmaniam Sundaranar University, Tamil Nadu, India in 2012. Her research interests include Digital Image Processing, Neural Networks and Data Mining.

M. Asha Paul

Asha Paul M. received her B.E. degree in Computer Science and Engineering from Anna University, India, in 2008. Later, she received her M.E. degree in Computer Science and Engineering from Manonmaniam Sundaranar University, Tirunelveli, India in 2010. She received her Ph.D. from Manonmaniam Sundaranar University in 2021. Currently she is working as an Assistant Professor in the Department of Computer Science and Business systems, Francis Xavier Engineering College, Tirunelveli, Tamil Nadu, India. She has published many articles in reputed journals. Her research interests include image and Video Processing, Artificial Intelligence and Video Summarization.