Night time Surveillance in Communication Using YOLOv3
DOI:
https://doi.org/10.32628/IJSRSET2411592Keywords:
Mean Average Precision, YOLOv3, DL Models, Deep Learning, Single Shot DetectorAbstract
Nighttime surveillance presents significant challenges due to low visibility, varying lighting conditions, and interference from artificial light sources. Hence, this study proposed an effective object detection framework using the YOLOv3 model to enhance real-time monitoring in night surveillance applications, specifically within communication and security systems. YOLOv3's architecture, with its multi-scale detection and use of predefined anchor boxes, enables robust detection of objects under low-light environments and amidst light interference from vehicles and streetlights. The proposed system is tested on a night surveillance dataset, where it demonstrates high precision and speed in identifying objects, making it suitable for real-time applications. With Mean Average Precision (mAP) 87.9%, YOLOv3 effectively balances detection accuracy with inference time, ensuring minimal latency in live surveillance feeds. The results indicate that YOLOv3 outperforms traditional models such as Faster RCNN, particularly in detecting small objects under poor illumination. This approach offers a reliable solution for enhancing communication and security systems in nighttime surveillance scenarios.
Downloads
References
Shlhamer E, Long J, Darrell T. (2016) “Fully convolutional networks for semantic segmentation.” IEEE Transactions on Pattern Analysis &Machine Intelligence 79(10):1337-1342.
Girshick R, Donahue J, Darrell T, et al. (2014) “Rich feature hierarchies for accurate object detection and semantic segmentation.” IEEE Conference on Computer Vision and Pattern Recognition, USA: IEEE 580-587. DOI: https://doi.org/10.1109/CVPR.2014.81
Liu W, Auguelov D, Erhan D, et al. (2016) “SSD: Single Shot MultiBox Detector.” European Conference on Computer Vision (ECCV). San Francisco, CA, USA: IEEE Conference 6517-6525. DOI: https://doi.org/10.1007/978-3-319-46448-0_2
Redmon J, Divvals S, Grishick R, et al. (2016) “You Only Look Once: unified, real time object detection.” IEEE Conference on Computer Vision and Pattern Recognition, USA: IEEE 779-788. DOI: https://doi.org/10.1109/CVPR.2016.91
Zhang X Y, Ding Q H, Luo H B, et al. (2017) “Infrared dim target detection algorithm based on improved LCM.” Infrared and Laser Engineering 46(7): 0726002. DOI: https://doi.org/10.3788/IRLA201746.0726002
Redmon J, Farhadi A. (2018) “YOLOv3: An Incremental Improvement.” IEEE Conference on Computer Vision and Pattern Recognition. USA: IEEE 2311-2314.
Erhan D, Szegedy C, Toshev A, et al. (2014) “Scalable object detection using deep neural networks.” Proceedings of the IEEE conference on computer vision and pattern recognition.2147-2154. DOI: https://doi.org/10.1109/CVPR.2014.276
MCCULLOCH WS, PITTS W. (1943) “A logical calculus of the ideas immanent in nervous activity.” The bulletin of mathematical biophysics 5(4):115-133. DOI: https://doi.org/10.1007/BF02478259
Zeiler M D, Krishnan D, Taylor G W, et al. (2010)“Deconvolutional networks.” Computer Vision & Pattern Recognition. DOI: https://doi.org/10.1109/CVPR.2010.5539957
M. K. Pargi, B. Setiawan and Y. Kazama. (2019) “Classification of different vehicles in traffic using RGB and Depth images: A Fast RCNN Approach.” 2019 IEEE International Conference on Imaging Systems and Techniques (IST), Abu Dhabi, United Arab Emirates 1-6. B. Nemade, J. Nair, and B. Nemade, "Efficient GDP Growth Forecasting for India through a Novel Modified LSTM Approach," Communications on Applied Nonlinear Analysis, vol. 31, no. 2s, pp. 339-357, 2024. DOI: https://doi.org/10.1109/IST48021.2019.9010357
B. Marakarkandy, B. Nemade, S. Kelkar, P. V. Chandrika, V. A. Shirsath, and M. Mali, "Enhancing Multi-Channel Consumer Behavior Analysis: A Data-Driven Approach using the Optimized Apriori Algorithm," Journal of Electrical Systems, vol. 20, no. 2s, pp. 700–708, 2024. DOI: https://doi.org/10.52783/jes.1536
B. Nemade, N. Phadnis, A. Desai, and K. K. Mungekar, "Enhancing connectivity and intelligence through embedded Internet of Things devices," ICTACT Journal on Microelectronics, vol. 9, no. 4, pp. 1670-1674, Jan. 2024, doi: 10.21917/ijme.2024.0289.
Y Li, K He, and J Sun. (2016) “R-fcn: Object detection via region based fully convolutional networks.” In Advances in Neural Information Processing systems, 630-645.
Z. Wang, Z. Cheng, H. Huang and J. Zhao, (2019) “ShuDA-RFBNet for Real-time Multi-task Traffic Scene Perception.” 2019 Chinese Automation Congress (CAC), Hangzhou, China 305-310. DOI: https://doi.org/10.1109/CAC48633.2019.8997236
Sandler M, Howard A, Zhu M, et al. (2018) “MobileNetV2: inverted residuals and linear bottlenecks.” IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE 4510-4520. DOI: https://doi.org/10.1109/CVPR.2018.00474
Ioffe S, Szegedy C. (2015) “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shife.” International Conference on Machine Learning. USA: ICML.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 International Journal of Scientific Research in Science, Engineering and Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.