Aerial Image Stitching Algorithm for Unmanned Aerial Vehicles Based on Improved ORB and PROSAC

Zhenyu Li; Yuan Tian; Fangjie Chen; Jun Han

doi:10.3788/LOP56.231003

Journals >Laser & Optoelectronics Progress >Volume 56 >Issue 23 >Page 231003 > Article

Laser & Optoelectronics Progress
Vol. 56, Issue 23, 231003 (2019)

Aerial Image Stitching Algorithm for Unmanned Aerial Vehicles Based on Improved ORB and PROSAC

Zhenyu Li^1、2, Yuan Tian³, Fangjie Chen^4、*, and Jun Han⁴

Author Affiliations

¹State Grid Power System Artificial Intelligence Joint Lab, State Grid Shandong Electric Power Company Electric Power Research Institute, Jinan, Shandong 250001, China

²Shandong Luneng Intelligent Technology Co., Ltd., Jinan, Shandong 250002, China

³State Grid Shandong Electric Power Company, Jinan, Shandong 250001, China

⁴School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China

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DOI: 10.3788/LOP56.231003 Cite this Article Set citation alerts

Zhenyu Li, Yuan Tian, Fangjie Chen, Jun Han. Aerial Image Stitching Algorithm for Unmanned Aerial Vehicles Based on Improved ORB and PROSAC[J]. Laser & Optoelectronics Progress, 2019, 56(23): 231003 Copy Citation Text

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Fig. 1. Determination of feature point main direction

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Fig. 2. Gradual-in and gradual-out image fusion method

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Fig. 3. Oxford VGG dataset. (a) Bikes; (b) boat; (c) graffiti

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Fig. 4. UAV aerial test images. (a) Test image 1 (b) test image 2; (c) test image 3

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Fig. 5. Comparison of stitching results of three groups of test images. (a) Stitching image 1; (b) stitching image 2; (c) stitching image 3

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Algorithm	Time /ms
Algorithm	Fig. 3(a)	Fig. 3(b)	Fig. 3(c)	Fig. 4(a)	Fig. 4(b)	Fig. 4(c)
SIFTSURFORBProposed	1.8750.4860.0790.186	1.3960.3550.0580.137	0.9230.2410.0420.103	7.6642.2750.4220.821	8.3212.0310.3870.933	7.8532.1320.4380.877

Table 1. Average time of each algorithm for feature point extraction

Algorithm	R_CM /%
Algorithm	Fig. 3(a)	Fig. 3(b)	Fig. 3(c)	Fig. 4(a)	Fig. 4(b)	Fig. 4(c)
SIFT+RANSACSURF+RANSACORB+RANSACORB+PROSACProposed	97.3596.2592.3795.0396.58	96.7994.8390.3193.7696.27	95.2895.0490.2193.4495.53	98.1296.6791.7394.3997.53	97.2395.4892.4795.2996.79	96.4695.7989.8693.5396.21

Table 2. Matching accuracy of each algorithm

Algorithm	Time /ms
Algorithm	Fig. 3(a)	Fig. 3(b)	Fig. 3(c)	Fig. 4(a)	Fig. 4(b)	Fig. 4(c)
SIFT+RANSAC	112.88	115.34	120.74	133.55	154.79	125.56
SURF+RANSAC	102.53	97.64	104.27	115.57	123.26	99.74
ORB+RANSAC	93.79	90.56	93.85	92.49	117.38	103.19
ORB+PROSAC	51.69	50.73	51.22	58.35	64.28	55.86
Proposed	53.87	56.42	55.79	64.39	73.54	60.83

Table 3. Matching time of each algorithm

Algorithm	E_RMS
Algorithm	Fig. 4(a)	Fig. 4(b)	Fig. 4(c)
SIFT+RANSACSURF+RANSACORB+RANSACORB+PROSACProposed	0.4790.5450.7170.6130.529	0.5450.6830.7410.6620.567	0.5270.6030.6920.6440.586

Table 4. Root mean square error of each algorithm

Zhenyu Li, Yuan Tian, Fangjie Chen, Jun Han. Aerial Image Stitching Algorithm for Unmanned Aerial Vehicles Based on Improved ORB and PROSAC[J]. Laser & Optoelectronics Progress, 2019, 56(23): 231003

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