Pre-Review Convolutional Neural Network for Detecting Object in Image Comprehensive Survey and Analysis

Fidelis Gonten; Fidelis  Nfwan; Abdulsalam  Ya’u Gital

doi:10.55537/jistr.v3i2.799

Authors

Fidelis Gonten Abubakar Tafawa Balewa University Bauchi, Nigeria
Fidelis Nfwan Abubakar Tafawa Balewa University Bauchi, Nigeria
Abdulsalam Ya’u Gital Abubakar Tafawa Balewa University Bauchi,

DOI:

https://doi.org/10.55537/jistr.v3i2.799

Abstract

The Convolutional neural network (CNN) has significantly exposed a great performances and growing desire in the field of image processing within the research community, through relevant innovations in object detection by magnificent capacity in transfer learning and feature learning. With the advancement of CNN in object detection, huge amount of data is process with great speed. In respect to CNN, object detection has greatly advanced and become popular in the research community, security experts, traffic experts, and remote sensing community etc. In this review, comprehensive study of various CNN architecture for object detection in images based on conventional approached, novelty, and achievement were analysed in details. Therefore, it is an important review on how to achieve high performance in object detection via CNN. We first introduced the basic idea on CNN models and their improvement in detecting object. Secondly, we review CNN and its variant such as, ResNet, VGG, GoogleNet and other CNN architectures. Thirdly, we mention some performance metrics used for object detection. Lastly, we analyse some main contribution of CNN algorithm with their remarkable achievement and further analyse the challenge and its future direction

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References

Y. Liu, Z. Zhang, X. Liu, L. Wang, and X. Xia, “Ore image classification based on small deep learning model: Evaluation and optimization of model depth, model structure and data size,” Miner. Eng., vol. 172, 2021, doi: 10.1016/j.mineng.2021.107020.

S. Li, Y. Yao, J. Hu, G. Liu, X. Yao, and J. Hu, “An ensemble stacked convolutional neural network model for environmental event sound recognition,” Appl. Sci., vol. 8, no. 7, 2018, doi: 10.3390/app8071152.

M. Buda, A. Maki, and M. A. Mazurowski, “A systematic study of the class imbalance problem in convolutional neural networks,” Neural Networks, vol. 106, 2018, doi: 10.1016/j.neunet.2018.07.011.

H. M. Al-Jawahry, A. H. A. Hussein, G. Sunil, M. A. Alkhafaji, and P. K. Pareek, “Faster Region-Convolutional Neural Network with ResNet50 for Video Stream Object Detection,” in 3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023, 2023. doi: 10.1109/ICMNWC60182.2023.10435864.

S. Sahu, R. Kumar, M. S. Pathan, J. Shafi, Y. Kumar, and M. F. Ijaz, “Movie Popularity and Target Audience Prediction Using the Content-Based Recommender System,” IEEE Access, vol. 10, 2022, doi: 10.1109/ACCESS.2022.3168161.

A. M. Ali, B. Benjdira, A. Koubaa, W. El-Shafai, Z. Khan, and W. Boulila, “Vision Transformers in Image Restoration: A Survey,” Sensors, vol. 23, no. 5, 2023, doi: 10.3390/s23052385.

U. S. Bhargavi et al., “Brain Tumor Detection Using Convolutional Neural Network,” in Communications in Computer and Information Science, 2023. doi: 10.1007/978-3-031-35641-4_40.

T. Sinha and B. Verma, “A Non-iterative Radial Basis Function Based Quick Convolutional Neural Network,” in Proceedings of the International Joint Conference on Neural Networks, 2020. doi: 10.1109/IJCNN48605.2020.9206798.

J. H. Yoon and B. Jang, “Evolution of Deep Learning-Based Sequential Recommender Systems: From Current Trends to New Perspectives,” IEEE Access, vol. 11, 2023, doi: 10.1109/ACCESS.2023.3281981.

M. Dhurkunde, N. Kadam, M. Trivedi, S. Maru, and P. Shirke, “Multi-class Brain Tumor Detection using Convolutional Neural Network.,” Grad. Res. Eng. Technol., 2023, doi: 10.47893/gret.2023.1158.

M. A. Mahjoubi, S. Hamida, O. El Gannour, B. Cherradi, A. El Abbassi, and A. Raihani, “Improved Multiclass Brain Tumor Detection using Convolutional Neural Networks and Magnetic Resonance Imaging,” Int. J. Adv. Comput. Sci. Appl., vol. 14, no. 3, 2023, doi: 10.14569/IJACSA.2023.0140346.

D. Kusumawati, A. A. Ilham, A. Achmad, and I. Nurtanio, “Vgg-16 and Vgg-19 Architecture Models in Lie Detection Using Image Processing,” in Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022, 2022. doi: 10.1109/ICITISEE57756.2022.10057748.

Y. Tao, “Image Style Transfer Based on VGG Neural Network Model,” in 2022 IEEE International Conference on Advances in Electrical Engineering and Computer Applications, AEECA 2022, 2022. doi: 10.1109/AEECA55500.2022.9918891.

C. Shu and X. Hu, “Improved Image Style Transfer Based on VGG-16 Convolutional Neural Network Model,” in Journal of Physics: Conference Series, 2023. doi: 10.1088/1742-6596/2424/1/012021.

Z. Zhao and S. Zhang, “Style Transfer Based on VGG Network,” Int. J. Adv. Network, Monit. Control., vol. 7, no. 1, 2022, doi: 10.2478/ijanmc-2022-0005.

N. I. Mahbub, F. Naznin, M. I. Hasan, S. M. R. Shifat, M. A. Hossain, and M. Z. Islam, “Detect Bangladeshi Mango Leaf Diseases Using Lightweight Convolutional Neural Network,” in 3rd International Conference on Electrical, Computer and Communication Engineering, ECCE 2023, 2023. doi: 10.1109/ECCE57851.2023.10101648.

S. Zhang, Y. H. Gong, and J. J. Wang, “The Development of Deep Convolution Neural Network and Its Applications on Computer Vision,” Jisuanji Xuebao/Chinese J. Comput., vol. 42, no. 3, 2019, doi: 10.11897/SP.J.1016.2019.00453.

I. Ahmed, M. Ahmad, A. Chehri, M. M. Hassan, and G. Jeon, “IoT Enabled Deep Learning Based Framework for Multiple Object Detection in Remote Sensing Images,” Remote Sens., vol. 14, no. 16, 2022, doi: 10.3390/rs14164107.

S. Arya and R. Singh, “A Comparative Study of CNN and AlexNet for Detection of Disease in Potato and Mango leaf,” in IEEE International Conference on Issues and Challenges in Intelligent Computing Techniques, ICICT 2019, 2019. doi: 10.1109/ICICT46931.2019.8977648.

N. Yamsani, M. B. Jabar, M. M. Adnan, A. H. A. Hussein, and S. Chakraborty, “Facial Emotional Recognition Using Faster Regional Convolutional Neural Network with VGG16 Feature Extraction Model,” in 3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023, 2023. doi: 10.1109/ICMNWC60182.2023.10435819.

Z. Gu, Z. Luo, M. Huang, Y. Cai, and S. Li, “A Graph-Convolutional-Network based Prototype Mixing Model for Few-shot Segmentation,” in Proceedings - 11th International Conference on Information Technology in Medicine and Education, ITME 2021, 2021. doi: 10.1109/ITME53901.2021.00028.

B. Azam et al., “Aircraft detection in satellite imagery using deep learning-based object detectors,” Microprocess. Microsyst., vol. 94, 2022, doi: 10.1016/j.micpro.2022.104630.

H. Lyu, “Research on Corrosion Recognition Method of Steel Based on Convolutional Neural Network,” in 2023 IEEE 6th International Conference on Information Systems and Computer Aided Education, ICISCAE 2023, 2023. doi: 10.1109/ICISCAE59047.2023.10393077.

D. O. Melinte and L. Vladareanu, “Facial expressions recognition for human–robot interaction using deep convolutional neural networks with rectified adam optimizer,” Sensors (Switzerland), vol. 20, no. 8, 2020, doi: 10.3390/s20082393.

A. J. Xiang, A. B. Huddin, M. F. Ibrahim, and F. H. Hashim, “An Oil Palm Loose Fruits Image Detection System using Faster R -CNN and Jetson TX2,” in Proceedings of the International Conference on Electrical Engineering and Informatics, 2021. doi: 10.1109/ICEEI52609.2021.9611111.

L. Yang et al., “An Improving Faster-RCNN With Multi-Attention ResNet for Small Target Detection in Intelligent Autonomous Transport With 6G,” IEEE Trans. Intell. Transp. Syst., vol. 24, no. 7, 2023, doi: 10.1109/TITS.2022.3193909.

A. Bagaskara and M. Suryanegara, “Evaluation of VGG-16 and VGG-19 Deep Learning Architecture for Classifying Dementia People,” in Proceedings - 2021 4th International Conference on Computer and Informatics Engineering: IT-Based Digital Industrial Innovation for the Welfare of Society, IC2IE 2021, 2021. doi: 10.1109/IC2IE53219.2021.9649132.

R. Dash, M. Bag, D. Pattnayak, A. Mohanty, and I. Dash, “Automated signature inspection and forgery detection utilizing VGG-16: a deep convolutional neural network,” in 2023 2nd International Conference on Ambient Intelligence in Health Care, ICAIHC 2023, 2023. doi: 10.1109/ICAIHC59020.2023.10431430.

W. Zhang, “Computer Identification of Mango Leaf Disease Based on Adversarial Denoising Autoencoder Model,” in Proceedings - 2022 International Conference on Machine Learning and Intelligent Systems Engineering, MLISE 2022, 2022. doi: 10.1109/MLISE57402.2022.00101.

Venkatesh, Y. Nagaraju, T. S. Sahana, S. Swetha, and S. U. Hegde, “Transfer Learning based Convolutional Neural Network Model for Classification of Mango Leaves Infected by Anthracnose,” in 2020 IEEE International Conference for Innovation in Technology, INOCON 2020, 2020. doi: 10.1109/INOCON50539.2020.9298269.

Z. Zhang, Q. Jiang, Y. Zhan, X. Hou, Y. Zheng, and Y. Cui, “VAE-GAN Data Enhancement Networks-based Model for Rolling Bearing Few-shot Fault Classification,” Yuanzineng Kexue Jishu/Atomic Energy Sci. Technol., vol. 57, 2023, doi: 10.7538/yzk.2023.youxian.0198.

A. N. Yumang, C. J. N. Samilin, and J. C. P. Sinlao, “Detection of Anthracnose on Mango Tree Leaf Using Convolutional Neural Network,” in 2023 15th International Conference on Computer and Automation Engineering, ICCAE 2023, 2023. doi: 10.1109/ICCAE56788.2023.10111489.

N. Srivastava, S. Ruhil, and G. Kaushal, “Music Genre Classification using Convolutional Recurrent Neural Networks,” in 2022 IEEE 6th Conference on Information and Communication Technology, CICT 2022, 2022. doi: 10.1109/CICT56698.2022.9997961.

M. Kiruthiga Devi, U. Surya, K. Unnamalai, and R. K. Tharani, “Treatment for Insomnia using Music Genre prediction using Convolutional Recurrent Neural Network,” in 2022 1st International Conference on Computational Science and Technology, ICCST 2022 - Proceedings, 2022. doi: 10.1109/ICCST55948.2022.10040279.

Y. Li, “Research on Application of Convolutional Neural Network in Intrusion Detection,” in Proceedings - 2020 7th International Forum on Electrical Engineering and Automation, IFEEA 2020, 2020. doi: 10.1109/IFEEA51475.2020.00153.

P. Mittal, A. Sharma, R. Singh, and V. Dhull, “Dilated convolution based RCNN using feature fusion for Low-Altitude aerial objects,” Expert Syst. Appl., vol. 199, 2022, doi: 10.1016/j.eswa.2022.117106.

H. Long, Y. Chung, Z. Liu, and S. Bu, “Object Detection in Aerial Images Using Feature Fusion Deep Networks,” IEEE Access, vol. 7, 2019, doi: 10.1109/ACCESS.2019.2903422.

Y. Hu, X. Li, N. Zhou, L. Yang, L. Peng, and S. Xiao, “A Sample Update-Based Convolutional Neural Network Framework for Object Detection in Large-Area Remote Sensing Images,” IEEE Geosci. Remote Sens. Lett., vol. 16, no. 6, 2019, doi: 10.1109/LGRS.2018.2889247.

L. Nanni, G. Maguolo, S. Brahnam, and M. Paci, “An ensemble of convolutional neural networks for audio classification,” Appl. Sci., vol. 11, no. 13, 2021, doi: 10.3390/app11135796.

R. Aulia, Suendri, and M. Alda, “Build Android-Based Go Service Electronic Applications Using React Native Framework and Firebase Realtime Database,” J. Inf. Syst. Technol. Res., vol. 2, no. 3, pp. 102–114, 2023, doi: 10.55537/jistr.v2i3.615.

Kaggle.com., “No Title.” [Online]. Available: https://www.kaggle.com/datasets/andrewmvd/early-diabetes-classification

T. Liu, L. Yang, and D. Lunga, “Change detection using deep learning approach with object-based image analysis,” Remote Sens. Environ., vol. 256, 2021, doi: 10.1016/j.rse.2021.112308.

A. Ikhwan, R. A. A. Raof, P. Ehkan, Y. Yacob, and M. Syaifuddin, “Data Security Implementation using Data Encryption Standard Method for Student Values at the Faculty of Medicine, University of North Sumatra,” J. Phys. Conf. Ser., vol. 1755, no. 1, 2021, doi: 10.1088/1742-6596/1755/1/012022.

A. A. Kabanov, “Application of Support Vector Machines to the Multiclass Classification Electromyography Signal Patterns,” in Proceedings of the 2021 15th International Scientific-Technical Conference on Actual Problems of Electronic Instrument Engineering, APEIE 2021, 2021. doi: 10.1109/APEIE52976.2021.9647434.

P. C. Upadhyay, L. Karanam, J. A. Lory, and G. N. Desouza, “Classifying Cover Crop Residue from RGB Images: A Simple SVM versus a SVM Ensemble,” in 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings, 2021. doi: 10.1109/SSCI50451.2021.9660147.