Citation

Yeh, Chia Cheng and Chang, Yang Lang and Alkhaleefah, Mohammad and Hsu, Pai Hui and Eng, Weiyong and Koo, Voon Chet and Huang, Bormin and Chang, Lena (2021) YOLOv3-based matching approach for roof region detection from drone images. Remote Sensing, 13 (1). pp. 1-23. ISSN 2072-4292

Text YOLOv3-Based Matching Approach for Roof Region Detection from Drone Images.pdf Restricted to Repository staff only Download (17MB)

Abstract

Due to the large data volume, the UAV image stitching and matching suffers from high computational cost. The traditional feature extraction algorithms—such as Scale-Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF), and Oriented FAST Rotated BRIEF (ORB)—require heavy computation to extract and describe features in high-resolution UAV images. To overcome this issue, You Only Look Once version 3 (YOLOv3) combined with the traditional feature point matching algorithms is utilized to extract descriptive features from the drone dataset of residential areas for roof detection. Unlike the traditional feature extraction algorithms, YOLOv3 performs the feature extraction solely on the proposed candidate regions instead of the entire image, thus the complexity of the image matching is reduced significantly. Then, all the extracted features are fed into Structural Similarity Index Measure (SSIM) to identify the corresponding roof region pair between consecutive image sequences. In addition, the candidate corresponding roof pair by our architecture serves as the coarse matching region pair and limits the search range of features matching to only the detected roof region. This further improves the feature matching consistency and reduces the chances of wrong feature matching. Analytical results show that the proposed method is 13× faster than the traditional image matching methods with comparable performance.

Item Type:	Article
Uncontrolled Keywords:	Algorithms, Image matching, deep learning
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	10 Mar 2021 01:49
Last Modified:	01 Jan 2023 15:00
URII:	http://shdl.mmu.edu.my/id/eprint/8571

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