Login Register Cart 0
Generic placeholder image

Recent Advances in Electrical & Electronic Engineering

Editor-in-Chief

ISSN (Print): 2352-0965
ISSN (Online): 2352-0973

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Information Extraction of the Vehicle from High-Resolution Remote Sensing Image Based on Convolution Neural Network

Author(s): Yanmei Wang, Fei Peng, Mingyu Lu and Mohammad Asif Ikbal*

Volume 16, Issue 2, 2023

Published on: 04 October, 2022

Page: [168 - 177] Pages: 10

DOI: 10.2174/2352096515666220820174654

Price: $65

Abstract

Aims: To effectively detect vehicle targets in remote sensing images, it can be widely used in traffic management, route planning, and vehicle flow detection. YOLOv3 deep learning neural network, which mainly studies the vehicle target detection in remote sensing images and carries out the target detection suitable for the characteristics of remote sensing images.

Objective: This paper studies the information extraction of vehicle high-resolution remote sensing images based on a convolution neural network.

Methods: The YOLOv3 network model of vehicle target detection in satellite remote sensing images is optimized. The iterations are set to 50002000045000, and the learning rate is 0.001. At the same time, the comparative experiments of RCNN, Fast RCNN, fast RCNN, and yolov3 network models are carried out.

Results: The ca-yolov3 network model can be applied to target detection in satellite images. After 40500 times of learning, the loss function value of the model is reduced to about 0.011.

Conclusion: The IOU value of the model also has a good performance in the training process, which makes the yolov3 neural network model more accurate in the image small target detection.

Keywords: Convolution neural network, target detection, remote sensing image, yolov3 network mode, information extraction, traffic, detection.

« Previous Next »
[1]
A. Sharma, P. Cholda, R. Kumar, and G. Dhiman, "Risk-aware optimized quickest path computing technique for critical routing services", Comput. Electr. Eng., vol. 95, p. 107436, 2021.
[http://dx.doi.org/10.1016/j.compeleceng.2021.107436]
[2]
A. Sharma, and R. Kumar, "Computation of the reliable and quickest data path for healthcare services by using service-level agreements and energy constraints", Arab. J. Sci. Eng., vol. 44, no. 11, pp. 9087-9104, 2019.
[http://dx.doi.org/10.1007/s13369-019-03836-4]
[3]
A. Sharma, and R. Kumar, "A constrained framework for contextaware remote E‐healthcare (CARE) services", Trans. Emerg. Telecommun. Technol., p. e3649, 2019.
[http://dx.doi.org/10.1002/ett.3649]
[4]
A. Sharma, and R. Kumar, "An optimal routing scheme for critical healthcare HTH services-an IOT perspective", In 2017 Fourth International Conference on Image Information Processing (ICIIP) 21-23 Dec 2017, Shimla, India, 2017, pp. 1-5
[5]
C. Cheng, and A. Sharma, ""Improved CNN license plate image recognition based on shark odor optimization algorithm"", Int. J. Syst. Assur. Eng. Manag., pp. 1-8, 2021.
[http://dx.doi.org/10.1007/s13198-021-01309-2]
[6]
N. Wang, X. Zhang, and A. Sharma, "A research on HMM based speech recognition in spoken English,", Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering), vol. 14, no. 6, pp. 617-626, 2021.
[http://dx.doi.org/10.2174/2352096514666210413122517]
[7]
Y. Zhang, A. Asthana, S. Asthana, S. Khanna, and I.C. Mihai, "Design of intelligent acquisition system for moving object trajectory data under cloud computing", J. Intell. Syst., vol. 30, no. 1, pp. 763-773, 2021.
[http://dx.doi.org/10.1515/jisys-2020-0152]
[8]
W. Zhou, S. Newsam, C. Li, and Z. Shao, "Learning low dimensional convolutional neural networks for high-resolution remote sensing image retrieval", Remote Sens., vol. 9, no. 5, p. 489, 2016.
[http://dx.doi.org/10.3390/rs9050489]
[9]
Z. Chen, B. Cong, Z. Hua, K. Cengiz, and M. Shabaz, "Application of clustering algorithm in complex landscape farmland synthetic aperture radar image segmentation", J. Intell. Syst., vol. 30, no. 1, pp. 1014-1025, 2021.
[http://dx.doi.org/10.1515/jisys-2021-0096]
[10]
Y. Yan, Y. Zhang, A. Sharma, and J.F. Al-Amri, "Evaluation of suitability of urban land using GIS technology", Sustainability, vol. 13, no. 19, p. 10521, 2021.
[http://dx.doi.org/10.3390/su131910521]
[11]
L. Ting, M. Khan, A. Sharma, and M.D. Ansari, "A secure framework for IoT-based smart climate agriculture system: Toward blockchain and edge computing", J. Intell. Syst., vol. 31, no. 1, pp. 221-236, 2022.
[http://dx.doi.org/10.1515/jisys-2022-0012]
[12]
Z. Dong, M. Wang, Y. Wang, Y. Zhu, and Z. Zhang, "Object detection in high resolution remote sensing imagery based on convolutional neural networks with suitable object scale features", IEEE Trans. Geosci. Remote Sens., vol. 58, no. 3, pp. 2104-2114, 2020.
[http://dx.doi.org/10.1109/TGRS.2019.2953119]
[13]
L.U. Heng, F.U. Xiao, C. Liu, L.I. Long-Guo, H.E. Yu-Xin, and L.I. Nai-Wen, "Cultivated land information extraction in UAV imagery based on deep convolutional neural network and transfer learning", J. Mt. Sci., vol. 14, no. 004, pp. 731-741, 2017.
[http://dx.doi.org/10.1007/s11629-016-3950-2]
[14]
F. Li, R. Feng, W. Han, and L. Wang, "High-resolution remote sensing image scene classification via key filter bank based on convolutional neural network", IEEE Trans. Geosci. Remote Sens., vol. 58, no. 11, pp. 8077-8092, 2020.
[http://dx.doi.org/10.1109/TGRS.2020.2987060]
[15]
P.K. Singh, and A. Sharma, "An intelligent WSN-UAV-based IoT framework for precision agriculture application", Comput. Electr. Eng., vol. 100, p. 107912, 2022.
[http://dx.doi.org/10.1016/j.compeleceng.2022.107912]
[16]
H. Wang, L. Hao, A. Sharma, and A. Kukkar, "Automatic control of computer application data processing system based on artificial intelligence", J. Intell. Syst., vol. 31, no. 1, pp. 177-192, 2022.
[http://dx.doi.org/10.1515/jisys-2022-0007]
[17]
H. Zeng, G. Dhiman, A. Sharma, A. Sharma, and A. Tselykh, "An IoT and Blockchain based approach for the smart water management system in agriculture", Expert Syst., p. e12892, 2021.
[http://dx.doi.org/10.1111/exsy.12892]
[18]
A. Sharma, and P.K. Singh, "UAV&‐based framework for effective data analysis of forest fire detection using 5G networks: An effective approach towards smart cities solutions", Int. J. Commun. Syst., p. e4826, 2021.
[http://dx.doi.org/10.1002/dac.4826]
[19]
A. Sharma, P.K. Singh, and Y. Kumar, "An integrated fire detection system using IoT and image processing technique for smart cities", Sustain Cities Soc., vol. 61, p. 102332, 2020.
[http://dx.doi.org/10.1016/j.scs.2020.102332]
[20]
A. Vsm, A. Alk, A. Bkg, B. Hlfds, and C. Caa, "Exploring multiscale object-based convolutional neural network (multi-OCNN) for remote sensing image classification at high spatial resolution", ISPRS J. Photogramm. Remote Sens., vol. 168, pp. 56-73, 2020.
[http://dx.doi.org/10.1016/j.isprsjprs.2020.08.004]
[21]
Y. Li, B. Peng, L. He, K. Fan, Z. Li, and L. Tong, "Road extraction from unmanned aerial vehicle remote sensing images based on improved neural networks", Sensors, vol. 19, no. 19, p. 4115, 2019.
[http://dx.doi.org/10.3390/s19194115] [PMID: 31547609]
[22]
Q. Tao, Q. Zhang, and S. Sun, "Vehicle detection from high-resolution aerial images using spatial pyramid pooling-based deep convolutional neural networks", Multimedia Tools Appl., vol. 76, no. 20, pp. 1-13, 2017.
[23]
X. Chen, R.X. Gong, L.L. Xie, S. Xiang, C.L. Liu, and C.H. Pan, "Building regional covariance descriptors for vehicle detection", Geosci. Remote Sens. Lett., IEEE, vol. 14, no. 4,, pp. 524-528, 2017.
[http://dx.doi.org/10.1109/LGRS.2017.2653772]
[24]
L. Li, L. Han, H. Hu, Z. Liu, and H. Cao, "Standardized object-based dual CNNS for very high-resolution remote sensing image classification and standardization combination effect analysis", Int. J. Remote Sens., vol. 41, no. 17, pp. 6635-6663, 2020.
[http://dx.doi.org/10.1080/01431161.2020.1742946]
[25]
D. Pan, M. Zhang, and B. Zhang, "A generic FCN-based approach for the road-network extraction from VHR remote sensing images - using OpenStreetMap as benchmarks", IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 14, pp. 2662-2673, 2021.
[http://dx.doi.org/10.1109/JSTARS.2021.3058347]
[26]
S. Gennaro, A. Matese, B. Gioli, P. Toscano, A. Zaldei, and A. Palliotti, "Multisensor approach to assess vineyard thermal dynamics combining high-resolution Unmanned Aerial Vehicle (UAV) remote sensing and Wireless Sensor Network (WSN) proximal sensing", Sci. Hortic., vol. 221, pp. 83-87, 2017.
[http://dx.doi.org/10.1016/j.scienta.2017.04.024]
[27]
Q. Guo, and Z. Wang, "A self-supervised learning framework for road centerline extraction from high resolution remote sensing images", IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 13, pp. 4451-4461, 2020.
[http://dx.doi.org/10.1109/JSTARS.2020.3014242]
[28]
X. Zhang, F. Zhang, Y. Qi, L. Deng, X. Wang, and S. Yang, "New research methods for vegetation information extraction based on visible light remote sensing images from an Unmanned Aerial Vehicle (UAV)", Int. J. Appl. Earth Obs. Geoinf., vol. 78, pp. 215-226, 2019.
[http://dx.doi.org/10.1016/j.jag.2019.01.001]
[29]
Y. Zhang, "Smart photogrammetric and remote sensing image processing for very high resolution optical images examples from the CRC-AGIP lab at UNB", J. Geodesy Geoinf. Sci., vol. 2, no. 2, p. 17, 2019.
[30]
M. Jaderberg, K. Simonyan, A. Vedaldi, and A. Zisserman, "Reading text in the wild with convolutional neural networks", Int. J. Comput. Vis., vol. 116, no. 1, pp. 1-20, 2016.
[http://dx.doi.org/10.1007/s11263-015-0823-z]
[31]
Y. Wang, J. Yue, Y. Dong, and Z. Hu, "Review on kernel based target tracking for autonomous driving", J. Inf. Process., vol. 24, no. 1, pp. 49-63, 2016.
[http://dx.doi.org/10.2197/ipsjjip.24.49] [PMID: 14714643]
[32]
H. Li, and G. Zhang, "Coastal atmospheric climate and vehicle file management based on high-resolution image recognition", Arab. J. Geosci., vol. 14, no. 11, pp. 1-16, 2021.
[http://dx.doi.org/10.1007/s12517-021-07402-x]
[33]
N. Tajbakhsh, J.Y. Shin, S.R. Gurudu, R.T. Hurst, C.B. Kendall, M.B. Gotway, and J. Liang, "Convolutional neural networks for medical image analysis: Full training or fine tuning?", IEEE Trans. Med. Imaging, vol. 35, no. 5, pp. 1299-1312, 2016.
[http://dx.doi.org/10.1109/TMI.2016.2535302] [PMID: 26978662]
[34]
C. Tao, L. Mi, Y. Li, J. Qi, and J. Zhang, "Scene context-driven vehicle detection in high-resolution aerial images", IEEE Trans. Geosci. Remote Sens., vol. 57, no. 10, pp. 7339-7351, 2019.
[http://dx.doi.org/10.1109/TGRS.2019.2912985]
[35]
J. Zhang, T. Chao, and Z. Zou, "An on-road vehicle detection method for high-resolution aerial images based on local and global structure learning", IEEE Geosci. Remote Sens. Lett., vol. 14, no. 8, pp. 1198-1202, 2017.
[http://dx.doi.org/10.1109/LGRS.2017.2701902]
[36]
Z. Chen, W. Cheng, H. Luo, H. Wang, Y. Chen, and C. Wen, "Vehicle detection in high-resolution aerial images based on fast sparse representation classification and multiorder feature", IEEE Trans. Intell. Transp. Syst., vol. 17, no. 8, pp. 2296-2309, 2016.
[http://dx.doi.org/10.1109/TITS.2016.2517826]
[37]
Q. Tan, J. Ling, J. Hu, X. Qin, and J. Hu, "Vehicle detection in high resolution satellite remote sensing images based on deep learning", IEEE Access, vol. 8, pp. 153394-153402, 2020.
[http://dx.doi.org/10.1109/ACCESS.2020.3017894]
[38]
S. Pereira, A. Pinto, V. Alves, and C.A. Silva, "Brain tumor segmentation using convolutional neural networks in MRI images", IEEE Trans. Med. Imaging, vol. 35, no. 5, pp. 1240-1251, 2016.
[http://dx.doi.org/10.1109/TMI.2016.2538465] [PMID: 26960222]
[39]
A. Nicolas, S.B. Le, and L. Sébastien, "Segment-before-detect: Vehicle detection and classification through semantic segmentation of aerial images", Remote Sens., vol. 9, no. 4, p. 368, 2017.
[http://dx.doi.org/10.3390/rs9040368]
[40]
M.A. Yucel, and R.Y. Turan, "Areal change detection and 3d modeling of mine lakes using high-resolution unmanned aerial vehicle images", Arab. J. Sci. Eng., vol. 41, no. 12, pp. 1-12, 2016.
[http://dx.doi.org/10.1007/s13369-016-2182-7]
[41]
H. Li, Z. Yu, X. Cai, and Z. Hu, "River terrace extraction based on unmanned aerial vehicle remote sensing", Earth Sci. J. China Uni. Geosci., vol. 42, no. 5, pp. 734-742, 2017.
[42]
B. Lu, and Y. He, "Species classification using Unmanned Aerial Vehicle (UAV)-acquired high spatial resolution imagery in a heterogeneous grassland", ISPRS J. Photogramm. Remote Sens., vol. 128, pp. 73-85, 2017.
[http://dx.doi.org/10.1016/j.isprsjprs.2017.03.011]
[43]
A. Shafiee, A. Nag, N. Muralimanohar, R. Balasubramonian, J.P. Strachan, and H. Miao, "Isaac: A convolutional neural network accelerator with in situ analog arithmetic in crossbars", Acm Sigarch Comput. Archit. News, vol. 44, no. 3, pp. 14-26, 2016.
[http://dx.doi.org/10.1145/3007787.3001139]

Rights & Permissions Print Cite
Article Metrics
1
© 2024 Bentham Science Publishers | Privacy Policy