TY - GEN
T1 - Anchored versus Anchorless Detector for Car Detection in Aerial Imagery
AU - Akshatha, K. R.
AU - Biswas, Subhrajyoti
AU - Karunakar, A. K.
AU - Satish Shenoy, B.
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/10/1
Y1 - 2021/10/1
N2 - With the increase in the traffic on roadways, traffic monitoring is the major need we have at this moment. Using UAVs for traffic monitoring has numerous advantages such as broader field of view, higher mobility, no effect on detected traffic, etc., however, variation in camera orientation, UAV height, cluttered background imposes challenges to this aerial object detection. To provide a UAV-based traffic monitoring solution, we have proposed a car detection system for UAV images using deep learning approaches. We compared the performance of the anchorless Fully Convolutional One Stage (FCOS) object detection algorithm with the popular YOLOv3 algorithm. The performance analysis of these models based on mean Average Precision (mAP) indicates that FCOS yields better results over YOLOv3, whereas in terms of computation speed YOLOv3 performed better.
AB - With the increase in the traffic on roadways, traffic monitoring is the major need we have at this moment. Using UAVs for traffic monitoring has numerous advantages such as broader field of view, higher mobility, no effect on detected traffic, etc., however, variation in camera orientation, UAV height, cluttered background imposes challenges to this aerial object detection. To provide a UAV-based traffic monitoring solution, we have proposed a car detection system for UAV images using deep learning approaches. We compared the performance of the anchorless Fully Convolutional One Stage (FCOS) object detection algorithm with the popular YOLOv3 algorithm. The performance analysis of these models based on mean Average Precision (mAP) indicates that FCOS yields better results over YOLOv3, whereas in terms of computation speed YOLOv3 performed better.
UR - http://www.scopus.com/inward/record.url?scp=85119475463&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85119475463&partnerID=8YFLogxK
U2 - 10.1109/GCAT52182.2021.9587621
DO - 10.1109/GCAT52182.2021.9587621
M3 - Conference contribution
AN - SCOPUS:85119475463
T3 - 2021 2nd Global Conference for Advancement in Technology, GCAT 2021
BT - 2021 2nd Global Conference for Advancement in Technology, GCAT 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd Global Conference for Advancement in Technology, GCAT 2021
Y2 - 1 October 2021 through 3 October 2021
ER -