399 265
Full Length Article
Fusion: Practice and Applications
Volume 7 , Issue 1, PP: 41-52 , 2022 | Cite this article as | XML | Html |PDF

Title

Earthworm Optimization with Deep Transfer Learning Enabled Aerial Image Classification Model in IoT Enabled UAV Networks

Authors Names :   Dr.R.PANDI SELVAM   1 *  

1  Affiliation :  Assistant Professor & Head, PG Department of Computer Science, Vidhyaa Giri College of Arts & Science Puduvayal, Karaikudi- 630 108, Sivaganga District, Tamilnadu, India

    Email :  pandiselvamraman@gmail.com



Doi   :   https://doi.org/10.54216/FPA.070104


Abstract :

Unmanned aerial vehicles (UAVs) can be placed effectively in offering high-quality services for Internet of Things (IoT) networks. It finds use in several applications such as smart city, smart healthcare, surveillance, environment monitoring, disaster management, etc. Classification of images captured by UAV networks, i.e., aerial image classification is a challenging task and can be solved by the design of artificial intelligence (AI) techniques. Therefore, this article presents an Earthworm Optimization with Deep Transfer Learning Enabled Aerial Image Classification (EWODTL-AIC) model in IoT enabled UAV networks. The major intention of the EWODTL-AIC technique is to effectually categorize different classes of aerial images captured by UAVs. The EWODTL-AIC technique initially employs AlexNet model as feature extractor for producing optimal feature vectors. Followed by, the hyperparameter values of the AlexNet model are decided by the utilization of earthworm optimization (EWO) algorithm. At last, the extreme gradient boosting (XGBoost) model is employed for the classification of aerial images. The experimental validation of the EWODTL-AIC model is performed using benchmark dataset. The extensive comparative analysis reported the better outcomes of the EWODTL-AIC technique over the other existing techniques

Keywords :

Unmanned aerial vehicles , Internet of things , Aerial images , Image classification , Deep learning , Parameter optimization.

References :

[1]       Kyrkou, C. and Theocharides, T., 2019, June. Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial Vehicles. In CVPR Workshops (pp. 517-525).

[2]       Boursianis, A.D., Papadopoulou, M.S., Diamantoulakis, P., Liopa-Tsakalidi, A., Barouchas, P., Salahas, G., Karagiannidis, G., Wan, S. and Goudos, S.K., 2020. Internet of things (IoT) and agricultural unmanned aerial vehicles (UAVs) in smart farming: a comprehensive review. Internet of Things, p.100187.

[3]       Islam, N., Rashid, M.M., Pasandideh, F., Ray, B., Moore, S. and Kadel, R., 2021. A review of applications and communication technologies for internet of things (Iot) and unmanned aerial vehicle (uav) based sustainable smart farming. Sustainability, 13(4), p.1821.

[4]       Zhang, H. and Hanzo, L., 2020. Federated learning assisted multi-UAV networks. IEEE Transactions on Vehicular Technology, 69(11), pp.14104-14109.

[5]       Gebrehiwot, A., Hashemi-Beni, L., Thompson, G., Kordjamshidi, P. and Langan, T.E., 2019. Deep convolutional neural network for flood extent mapping using unmanned aerial vehicles data. Sensors, 19(7), p.1486.

[6]       Li, Y., Qian, M., Liu, P., Cai, Q., Li, X., Guo, J., Yan, H., Yu, F., Yuan, K., Yu, J. and Qin, L., 2019. The recognition of rice images by UAV based on capsule network. Cluster Computing, 22(4), pp.9515-9524.

[7]       Munawar, H.S., Ullah, F., Qayyum, S., Khan, S.I. and Mojtahedi, M., 2021. UAVs in disaster management: Application of integrated aerial imagery and convolutional neural network for flood detection. Sustainability, 13(14), p.7547.

[8]       Cai, W., Wei, Z., Song, Y., Li, M. and Yang, X., 2021. Residual-capsule networks with threshold convolution for segmentation of wheat plantation rows in UAV images. Multimedia Tools and Applications, 80(21), pp.32131-32147.

[9]       Saha, A.K., Saha, J., Ray, R., Sircar, S., Dutta, S., Chattopadhyay, S.P. and Saha, H.N., 2018, January. IOT-based drone for improvement of crop quality in agricultural field. In 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 612-615). IEEE.

[10]    Haq, M.A., Rahaman, G., Baral, P. and Ghosh, A., 2021. Deep learning based supervised image classification using UAV images for forest areas classification. Journal of the Indian Society of Remote Sensing, 49(3), pp.601-606.

[11]    Kyrkou, C. and Theocharides, T., 2020. Emergencynet: Efficient aerial image classification for drone-based emergency monitoring using atrous convolutional feature fusion. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, pp.1687-1699.

[12]    Hua, Y., Mou, L. and Zhu, X.X., 2019. Recurrently exploring class-wise attention in a hybrid convolutional and bidirectional LSTM network for multi-label aerial image classification. ISPRS journal of photogrammetry and remote sensing, 149, pp.188-199.

[13]    Kareem, R.S.A., Ramanjineyulu, A.G., Rajan, R., Setiawan, R., Sharma, D.K., Gupta, M.K., Joshi, H., Kumar, A., Harikrishnan, H. and Sengan, S., 2021. Multilabel land cover aerial image classification using convolutional neural networks. Arabian Journal of Geosciences, 14(17), pp.1-18.

[14]    Lin, D., Lin, J., Zhao, L., Wang, Z.J. and Chen, Z., 2021. Multilabel aerial image classification with a concept attention graph neural network. IEEE Transactions on Geoscience and Remote Sensing, 60, pp.1-12.

[15]    Hidaka, M., Matsuoka, D., Sugiyama, D. and Murakami, K., 2022. Pixel-level image classification for detecting beach litter using a deep learning approach. Marine Pollution Bulletin, 175, p.113371.

[16]    Lu, S., Wang, S.H. and Zhang, Y.D., 2021. Detection of abnormal brain in MRI via improved AlexNet and ELM optimized by chaotic bat algorithm. Neural Computing and Applications, 33(17), pp.10799-10811.

[17]    Ghosh, I. and Roy, P.K., 2019, March. Application of earthworm optimization algorithm for solution of optimal power flow. In 2019 International Conference on Opto-Electronics and Applied Optics (Optronix) (pp. 1-6). IEEE.

[18]    Ogunleye, A. and Wang, Q.G., 2019. XGBoost model for chronic kidney disease diagnosis. IEEE/ACM transactions on computational biology and bioinformatics, 17(6), pp.2131-2140.

[19] http://weegee.vision.ucmerced.edu/datasets/landuse.html


Cite this Article as :
Dr.R.PANDI SELVAM, Earthworm Optimization with Deep Transfer Learning Enabled Aerial Image Classification Model in IoT Enabled UAV Networks, Fusion: Practice and Applications, Vol. 7 , No. 1 , (2022) : 41-52 (Doi   :  https://doi.org/10.54216/FPA.070104)