Vehicle detection from multi-modal aerial imagery using YOLOv3 with mid-level fusion

Mayur Dhanaraj; Manish Sharma; Tiyasa Sarkar; Srivallabha Karnam; Dimitris G. Chachlakis; Raymond Ptucha; Panos P. Markopoulos; Eli Saber

doi:10.1117/12.2558115

15 May 2020 Vehicle detection from multi-modal aerial imagery using YOLOv3 with mid-level fusion

Mayur Dhanaraj, Manish Sharma, Tiyasa Sarkar, Srivallabha Karnam, Dimitris G. Chachlakis, Raymond Ptucha, Panos P. Markopoulos, Eli Saber

Author Affiliations +

Proceedings Volume 11395, Big Data II: Learning, Analytics, and Applications; 1139506 (2020) https://doi.org/10.1117/12.2558115
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

Target detection is an important problem in remote-sensing with crucial applications in law-enforcement, military and security surveillance, search-and-rescue operations, and air traffic control, among others. Owing to the recently increased availability of computational resources, deep-learning based methods have demonstrated state-of- the-art performance in target detection from unimodal aerial imagery. In addition, owing to the availability of remote-sensing data from various imaging modalities, such as RGB, infrared, hyper-spectral, multi-spectral, synthetic aperture radar, and lidar, researchers have focused on leveraging the complementary information offered by these various modalities. Over the past few years, deep-learning methods have demonstrated enhanced performance using multi-modal data. In this work, we propose a method for vehicle detection from multi-modal aerial imagery, by means of a modified YOLOv3 deep neural network that conducts mid-level fusion. To the best of our knowledge, the proposed mid-level fusion architecture is the first of its kind to be used for vehicle detection from multi-modal aerial imagery using a hierarchical object detection network. Our experimental studies corroborate the advantages of the proposed method.

Conference Presentation

Citation Download Citation

Mayur Dhanaraj, Manish Sharma, Tiyasa Sarkar, Srivallabha Karnam, Dimitris G. Chachlakis, Raymond Ptucha, Panos P. Markopoulos, and Eli Saber "Vehicle detection from multi-modal aerial imagery using YOLOv3 with mid-level fusion", Proc. SPIE 11395, Big Data II: Learning, Analytics, and Applications, 1139506 (15 May 2020); https://doi.org/10.1117/12.2558115

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available