Object Detection for Night Vision using Deep Learning Algorithms
  IJCTT-book-cover
 		          
 
© 2023 by IJCTT Journal
Volume-71 Issue-2
Year of Publication : 2023
Authors : Dipali Bhabad, Surabhi Kadam, Tejal Malode, Girija Shinde, Dipak Bage
DOI :  10.14445/22312803/IJCTT-V71I2P113

How to Cite?

Dipali Bhabad, Surabhi Kadam, Tejal Malode, Girija Shinde, Dipak Bage, "Object Detection for Night Vision using Deep Learning Algorithms," International Journal of Computer Trends and Technology, vol. 71, no. 2, pp. 87-92, 2023. Crossref, https://doi.org/10.14445/22312803/IJCTT-V71I2P113

Abstract
Abnormal activity detection plays a very important role in surveillance applications. The existing research on surveillance for daytime has achieved better performance by detecting and tracking objects using deep learning algorithms. However, it is difficult to achieve the same performance for night vision mainly due to low illumination. Deep learning is a powerful machine learning technique in which the object detector automatically learns image features required for detection tasks. It is required to generate a model that detects objects under low illumination. The approach is to use thermal infrared images and detect external objects, if any, and classify whether it is human or animal in an isolation area. With the rapid growth of deep learning, more efficient techniques will be implemented to solve the problems of object detection using neural networks and deep learning.

Keywords
Object Detection, RetinaNet, SSD, Thermal Infrared Images, YOLO.

Reference

[1] Yuxuan Xiao et al., “Making of Night Vision: Object Detection Under Low-Illumination,” IEEE Access, vol. 8, pp. 123075-123086, 2020. Crossref, https://doi.org/10.1109/ACCESS.2020.3007610
[2] Muhammad Javed Iqbal et al., “Real-Time Surveillance Using Deep Learning,” Security and Communications Networks, 2021. Crossref, https://doi.org/10.1155/2021/6184756
[3] Aditya Lohia et al., Bibliometric Analysis of One-stage and Two-stage Object Detection, University of Nebraska-Lincoln, 2021.
[4] Heena Patel, and Kishor P. Upla, “Night Vision Surveillance: Object Detection using Thermal and Visible Images,” 2020 International Conference for Emerging Technology (INCET), 2020. Crossref, https://doi.org/10.1109/INCET49848.2020.9154066
[5] K. R. Akshatha et al., “Human Detection in Aerial Thermal Images Using Faster R-CNN and SSD Algorithms,” Electronics, vol. 11, no. 7, p. 1151, 2022. Crossref, https://doi.org/10.3390/electronics11071151
[6] Mate Kristo, Marina Ivasic-Kos, and Miran Pobar, “Thermal Object Detection in Difficult Weather Conditions Using YOLO,” IEEE Access, vol. 8, pp. 125459-125476, 2020. Crossref, https://doi.org/10.1109/ACCESS.2020.3007481
[7] Mohanad Al-Hasanat et al., “RetinaNet-based Approach for Object Detection and Distance Estimation in an Image,” International Journal on Communications Antenna and Propagation (IRECAP), vol. 11, no. 1, p. 19, 2021. Crossref, http://dx.doi.org/10.15866/irecap.v11i1.19341
[8] Navneet Dalal, and Bill Triggs, “Histograms of Oriented Gradients for Human Detection,” 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. Crossref, https://doi.org/10.1109/CVPR.2005.177
[9] Qasem Abu Al-Haija, Manaf Gharaibeh, and Ammar Odeh, “Detection in Adverse Weather Conditions for Autonomous Vehicles via Deep Learning,” AI Journal, vol. 3, no. 2, pp. 303-317, 2022. Crossref, https://doi.org/10.3390/ai3020019
[10] Wei Liu et al., “SSD: Single Shot MultiBox Detector,” Computer Vision and Pattern Recognition, 2015. Crossref, https://doi.org/10.48550/arXiv.1512.02325
[11] M. Maheswari, M. S. Josephine, and V. Jeyabalaraja, “YOLO Architecture-based Object Detection for Optimizing Performance in Video Streams,” International Journal of Engineering Trends and Technology, vol. 70, no. 11, pp. 187-196, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I11P220
[12] Jan Hosang, Rodrigo Benenson, and Bernt Schiele, “Learning Non-maximum Suppression,” Computer Vision and Pattern Recognition, 2017. Crossref, https://doi.org/10.48550/arXiv.1705.02950
[13] Tsung-Yi Lin et al., “Feature Pyramid Networks for Object Detection,” Computer Vision and Pattern Recognition, 2016. Crossref, https://doi.org/10.48550/arXiv.1612.03144
[14] Tsung-Yi Lin et al., “Focal Loss for Dense Object Detection,” Computer Vision and Pattern Recognition, 2017. Crossref, https://doi.org/10.48550/arXiv.1708.02002
[15] Aashish Bhandari et al., “Image Enhancement and Object Recognition for Night Vision Surveillance,” ICTRCET 18 at Bangaluru, 2018.
[16] Rupesh P.Raghatate et al, “Night Vision Techniques and Their Applications,” International Journal of Modern Engineering Research (IJMER), vol. 3, no. 2, pp. 816-820, 2013.
[17] D. Malarvizhi, V. Lavanya, and M. Nivetha priya, “Night Vision Technology,” International Journal for Scientific Research and Development, vol. 3, no. 8, 2017.
[18] Kai Hu et al., “A Marine Object Detection Algorithm Based on SSD and Feature Enhancement,” Complexity, 2020. Crossref, https://doi.org/10.1155/2020/5476142
[19] Sreehari Patibandla, Maruthavanan Archana, and Rama Chaithanya Tanguturi, “Object Tracking using Multi Adaptive Feature Extraction Technique,” International Journal of Engineering Trends and Technology, vol. 70, no. 6, pp. 279-286, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I6P229
[20] Pavan Sai Vemulapalli et al., “Multi-object Detection in Night Time,” Asian Journal of Convergence in Technology, vol. 5, no. 3, pp. 1- 7, 2019.
[21] Tanvir Ahmad et al., “Object Detection through Modified YOLO Neural Network,” Scientific Programming, 2020. Crossref, https://doi.org/10.1155/2020/8403262
[22] Muskan Choudhary et al., “Object Detection Using YOLO Models,” International Research Journal of Engineering and Technology, vol. 9, no. 5, pp. 3785-3789, 2022.
[23] Joaquın Royo Miquel et al., “RetinaNet Object Detector based on Analog-to-Spiking Neural Network Conversion,” Image and Video Processing, 2021. Crossref, https://doi.org/10.48550/arXiv.2106.05624
[24] R. Manasa, K Karibasappa, and J. Rajeshwari, “Autonomous Path Finder and Object Detection using an Intelligent Edge Detection Approach,” SSRG International Journal of Electrical and Electronics Engineering, vol. 9, no. 8, pp. 1-7, 2022. Crossref, https://doi.org/10.14445/23488379/IJEEE-V9I8P101
[25] Tsung-Yi Lin et al., “Feature Pyramid Networks for Object Detection,” in Processing IEEE Conference on Computer Visison Pattern Recognition (CVPR), 2016. Crossref, https://doi.org/10.48550/arXiv.1612.03144
[26] Tsung-Yi Lin et al., “Feature Pyramid Networks for Object Detection,” in Processing IEEE Conference on Computer Visison Pattern Recognition (CVPR), 2016. Crossref, https://doi.org/10.48550/arXiv.1612.03144
[27] Wu Zheng et al., “CIA-SSD: Confident IoU-Aware Single-Stage Object Detector from Point Cloud,” in Processing IEEE Conference on Computer Visison Pattern Recognition (CVPR), 2020. Crossref, https://doi.org/10.48550/arXiv.2012.03015
[28] Amit Tiwari, and Jalaj Gupta, “A Simulation of Night Vision Technology Aided with AI,” AKGEC International Journal of Technology, vol. 12, no. 1, 2022.
[29] Pranav Adarsh, Pratibha Rathi, and Manoj Kumar, “YOLO v3-Tiny: Object Detection and Recognition using One Stage Improved Model,” 2020 6th International Conference on Advanced Computing and Communication Systems, 2020. Crossref, https://doi.org/10.1109/ICACCS48705.2020.9074315