Automatic Object Detection on Aerial Images Using Convolutional Neural Networks

Authors

  • Jasdeep Singh RIMT University, Mandi Gobindgarh, Punjab, India Author

Keywords:

Aerial images, Automatic, Convolutional Neural Networks (CNNs), Convolutional neural network, Object detection

Abstract

 Large quantities of aerial and satellite  images are being acquired on a daily basis. Many  practical applications may benefit from the analysis of  such huge amounts of data. We propose an automated  content-based analysis of aerial photography in this letter,  which may be used to identify and label arbitrary objects  or areas in high-resolution pictures. We developed a  convolutional neural network-based approach for  automated object identification for this purpose. In the  tasks of aerial picture classification and object  identification, a new two-stage method for network  training is developed and validated. First, we used the UC  Merced data set of aerial pictures to evaluate the  suggested training method, and we were able to obtain an  accuracy of about 98.6%. Second, a technique for  automatically detecting objects was developed and tested.  For GPGPU implementation, a processing time of  approximately 30 seconds was needed for one aerial  picture of size 5000 x 5000 pixels. 

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References

Andres L, Boateng K, Borja-Vega C, Thomas E. A review of in-situ and remote sensing technologies to monitor water and sanitation interventions. Water (Switzerland). 2018;

Wei L, Zhang Y, Zhao Z, Zhong X, Liu S, Mao Y, et al. Analysis of mining waste dump site stability based on multiple remote sensing technologies. Remote Sens. 2018;

Singh D. Robust controlling of thermal mixing procedure by means of sliding type controlling. Int J Eng Adv Technol. 2019;

Ghai W, Kumar S, Athavale VA. Using gaussian mixtures on triphone acoustic modelling-based punjabi continuous speech recognition. In: Advances in Intelligent Systems and Computing. 2021.

Khatri M, Kumar A. Stability Inspection of Isolated Hydro Power Plant with Cuttlefish Algorithm. In: 2020 International Conference on Decision Aid Sciences and Application, DASA 2020. 2020.

Solanki MS, Goswami L, Sharma KP, Sikka R. Automatic Detection of Temples in consumer Images using histogram of Gradient. In: Proceedings of 2019 International Conference on Computational Intelligence and Knowledge Economy, ICCIKE 2019. 2019.

Anand V. Photovoltaic actuated induction motor for driving electric vehicle. Int J Eng Adv Technol. 2019;8(6 Special Issue 3):1612–4.

Paravolidakis V, Ragia L, Moirogiorgou K, Zervakis ME. Automatic coastline extraction using edge detection and optimization procedures. Geosci. 2018;

Guo W, Yang W, Zhang H, Hua G. Geospatial object detection in high resolution satellite images based on multi scale convolutional neural network. Remote Sens. 2018;

Deng Z, Sun H, Zhou S, Zhao J, Lei L, Zou H. Multi-scale object detection in remote sensing imagery with convolutional neural networks. ISPRS J Photogramm Remote Sens. 2018;

Kumar A, Jain A. Image smog restoration using oblique gradient profile prior and energy minimization. Front Comput Sci. 2021;

Gupta N, Vaisla KS, Jain A, Kumar A, Kumar R. Performance Analysis of AODV Routing for Wireless Sensor Network in FPGA Hardware. Comput Syst Sci Eng. 2021;

Kumar Gola K, Chaurasia N, Gupta B, Singh Niranjan D. Sea lion optimization algorithm based node deployment strategy in underwater acoustic sensor network. Int J

Commun Syst. 2021;

Kumari N, Kr. Bhatt A, Kr. Dwivedi R, Belwal R. Hybridized approach of image segmentation in classification of fruit mango using BPNN and discriminant analyzer. Multimed Tools Appl. 2021;

Jain A, Kumar A. Desmogging of still smoggy images using a novel channel prior. J Ambient Intell Humaniz Comput. 2021;

Goel S, Dwivedi RK, Sharma A. Analysis of social network using data mining techniques. In: Proceedings of the 2020 9th International Conference on System Modeling and Advancement in Research Trends, SMART 2020. 2020.

Younis S, Ahsan A. Know Your Stars before They Fall Apart: A Social Network Analysis of Telecom Industry to Foster Employee Retention Using Data Mining Technique. IEEE Access. 2021;

Sevo I, Avramovic A. Convolutional neural network based automatic object detection on aerial images. IEEE Geosci Remote Sens Lett. 2016;

Lu J, Ma C, Li L, Xing X, Zhang Y, Wang Z, et al. A Vehicle Detection Method for Aerial Image Based on YOLO. J Comput Commun. 2018;

Lin TY, Cui Y, Belongie S, Hays J. Learning deep representations for ground-to-aerial geolocalization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2015.

Qayyum A, Saad NM, Kamel N, Malik AS. Deep convolutional neural network processing of aerial stereo imagery to monitor vulnerable zones near power lines. J Appl Remote Sens. 2018;

Kumar S, Kumar K, Pandey AK. Dynamic Channel Allocation in Mobile Multimedia Networks Using Error Back Propagation and Hopfield Neural Network (EBP HOP). In: Procedia Computer Science. 2016.

Banerjee K, Prasad RA. Reference based inter chromosomal similarity based DNA sequence compression algorithm. In: Proceeding - IEEE International Conference on Computing, Communication and Automation, ICCCA 2017. 2017.

Verma S, Biswas R, Singh JB. Extension of superblock technique to hyperblock using predicate hierarchy graph. In: Communications in Computer and Information Science. 2010.

Yang C, Liu H, Wang S, Liao S. Remote sensing image classification using extreme learning machine-guided collaborative coding. Multidimens Syst Signal Process. 2017;

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Published

2021-11-30

Issue

Vol. 9 No. 6 (2021): International Journal of Innovative Research in Computer Science and Technology (Nov) 2021

Section

Articles

How to Cite

Automatic Object Detection on Aerial Images Using Convolutional Neural Networks . (2021). International Journal of Innovative Research in Computer Science & Technology, 9(6), 285–289. Retrieved from https://acspublisher.com/journals/index.php/ijircst/article/view/11215