Revolutionizing Home-Office Call Centers: Object Recognition for Performance and Data Security
Article Sidebar
Main Article Content
Abstract
Modern call centers operate within complex ecosystems where digitalization, automation, and surveillance technologies intersect. These advancements enable multi-channel communication, personalized services, and proactive customer support. Moving beyond traditional phone-based models, modern call centers leverage digital tools to enhance operational efficiency and customer experience. One of the key technologies driving this transformation is image processing techniques. These technologies automate tasks, minimizing human intervention and optimizing workflow. With the rise of home-office work setups, physical workspaces have become less common, and the boundaries between work and personal life have blurred. This situation causes employees to feel less supervised, leading to inefficient use of work hours and potential data breaches. This project aims to protect home-office employees' performance and data security using image processing technology, specifically object recognition and detection methods. The goal is to prevent issues such as virtual idleness, unauthorized data recording, and behaviors against workplace culture without violating employee privacy. By detecting objects such as phones, pens, paper, cameras, tablets, and cameras, behaviors that don't align with company culture will be prevented, and data privacy violations will be avoided. The proposed system demonstrates high performance, with object recognition algorithms achieving approximately 90% accuracy.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
Sapkota, R., Meng, Z., Churuvija, M., Du, X., Ma, Z., & Karkee, M. (2024). Comprehensive performance evaluation of yolo11, yolov10, yolov9 and yolov8 on detecting and counting fruitlet in complex orchard environments. arXiv preprint arXiv:2407.12040. DOI: https://doi.org/10.32388/E9Y7XI
Kayaalp, K. and A.A. Süzen, Derin Öğrenme. Derin Öğrenme ve Türkiye’deki Uygulamaları, Adıyaman, Türkiye: İKSAD Yayınevi, 2018: p. 25-28.
Wang, Y.; Yan, X.; Zhang, K.; Gong, L.; Xie, H.; Wang, F.L.; Wei, M. TogetherNet: Bridging Image Restoration and Object Detection Together via Dynamic Enhancement Learning. In Proceedings of the Computer Graphics Forum; Wiley Online Library: Hoboken, NJ, USA, 2022; Volume 41, pp. 465–476. DOI: https://doi.org/10.1111/cgf.14692
Li, W. Vehicle Detection in Foggy Weather Based on an Enhanced YOLO Method. In Proceedings of the Journal of Physics: Conference Series; IOP Publishing: Bristol, UK, 2022; Volume 2284, p. 012015. DOI: https://doi.org/10.1088/1742-6596/2284/1/012015
Hassaballah, M.; Kenk, M.A.; Muhammad, K.; Minaee, S. Vehicle Detection and Tracking in Adverse Weather Using a Deep Learning Framework. IEEE Trans. Intell. Transp. Syst. 2020, 22, 4230–4242. DOI: https://doi.org/10.1109/TITS.2020.3014013
Li, B.; Ren, W.; Fu, D.; Tao, D.; Feng, D.; Zeng, W.; Wang, Z. Benchmarking Single-Image Dehazing and Beyond. IEEE Trans. Image Process. 2018, 28, 492–505 DOI: https://doi.org/10.1109/TIP.2018.2867951
Kaur, R.; Singh, S. A Comprehensive Review of Object Detection with Deep Learning. Digit. Signal Process. 2023, 132, 103812. DOI: https://doi.org/10.1016/j.dsp.2022.103812
Talaat, A.S.; El-Sappagh, S. Enhanced Aerial Vehicle System Techniques for Detection and Tracking in Fog, Sandstorm, and Snow Conditions. J. Supercomput. 2023, 79, 15868–15893. DOI: https://doi.org/10.1007/s11227-023-05245-9
Wang, R.; Zhao, H.; Xu, Z.; Ding, Y.; Li, G.; Zhang, Y.; Li, H. Real-Time Vehicle Target Detection in Inclement Weather Conditions Based on YOLOv4. Front. Neurorobot. 2023, 17, 1058723. DOI: https://doi.org/10.3389/fnbot.2023.1058723
Liu, W.; Ren, G.; Yu, R.; Guo, S.; Zhu, J.; Zhang, L. Image-Adaptive YOLO for Object Detection in Adverse Weather Conditions. In Proceedings of the AAAI Conference on Artificial Intelligence, Philadelphia, PA, USA, 27 February–2 March 2022; Volume 36, pp. 1792–1800. DOI: https://doi.org/10.1609/aaai.v36i2.20072
Zhang, H.; Sehab, R.; Azouigui, S.; Boukhnifer, M. Application and Comparison of Deep Learning Methods to Detect Night-Time Road Surface Conditions for Autonomous Vehicles. Electronics 2022, 11, 786. DOI: https://doi.org/10.3390/electronics11050786
Farid, A.; Hussain, F.; Khan, K.; Shahzad, M.; Khan, U.; Mahmood, Z. A Fast and Accurate Real-Time Vehicle Detection Method Using Deep Learning for Unconstrained Environments. Appl. Sci. 2023, 13, 3059. DOI: https://doi.org/10.3390/app13053059
Xu, L.; Yan, W.; Ji, J. The Research of a Novel WOG-YOLO Algorithm for Autonomous Driving Object Detection. Sci. Rep. 2023, 13, 3699. DOI: https://doi.org/10.1038/s41598-023-30409-1
Li, C.; Zhou, H.; Liu, Y.; Yang, C.; Xie, Y.; Li, Z.; Zhu, L. Detection-Friendly Dehazing: Object Detection in Real-World Hazy Scenes. IEEE Trans. Pattern Anal. Mach. Intell. 2023, 45, 8284–8295. DOI: https://doi.org/10.1109/TPAMI.2023.3234976
Liu, H.; Sun, F.; Gu, J.; Deng, L. Sf-Yolov5: A Lightweight Small Object Detection Algorithm Based on Improved Feature Fusion Mode. Sensors 2022, 22, 5817. DOI: https://doi.org/10.3390/s22155817
Wang, H.; Xu, Y.; He, Y.; Cai, Y.; Chen, L.; Li, Y.; Sotelo, M.A.; Li, Z. YOLOv5-Fog: A Multiobjective Visual Detection Algorithm for Fog Driving Scenes Based on Improved YOLOv5. IEEE Trans. Instrum. Meas. 2022, 71, 1–12. DOI: https://doi.org/10.1109/TIM.2022.3196954
Xu, Q.; Wang, G.; Li, Y.; Shi, L.; Li, Y. A Comprehensive Swarming Intelligent Method for Optimizing Deep Learning-Based Object Detection by Unmanned Ground Vehicles. PLoS ONE 2021, 16, e0251339. DOI: https://doi.org/10.1371/journal.pone.0251339
Diwan, T.; Anirudh, G.; Tembhurne, J. V Object Detection Using YOLO: Challenges, Architectural Successors, Datasets and Applications. Multimed. Tools Appl. 2023, 82, 9243–9275. DOI: https://doi.org/10.1007/s11042-022-13644-y
Srivastava, S.; Divekar, A.V.; Anilkumar, C.; Naik, I.; Kulkarni, V.; Pattabiraman, V. Comparative Analysis of Deep Learning Image Detection Algorithms. J. Big Data 2021, 8, 66. DOI: https://doi.org/10.1186/s40537-021-00434-w
Redmon J., Divvala S., Girshick R., Farhadi A., You only look once: Unified, real-time object detection, IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 779-788, 26 Haziran1 Temmuz, 2016. DOI: https://doi.org/10.1109/CVPR.2016.91
Аманов, А. Н., & Абен, А. Б. (2024). GÖRÜNTÜ VERİLERİNDE NESNE TESPİTİ VE SINIFLANDIRILMASINDA KULLANILAN ÇEŞİTLİ ALGORİTMALARIN İNCELENMESİ VE UYGULAMASI. QA Iasaýı atyndaǵy Halyqaralyq qazaq-túrіk ýnıversıtetіnіń habarlary, 3(30), 62-74.
Di, B., Xiang, L., Daoqing, Y., & Kaimin, P. (2024). MARA-YOLO: An efficient method for multiclass personal protective equipment detection. IEEE Access. DOI: https://doi.org/10.1109/ACCESS.2024.3365504
Fatih Şengül1 , Kemal Adem 2 1Dept. of Defense Technologies, Sivas University of Science and Technology, Turkey 2Dept. of Computer Engineering, Sivas University of Science and Technology, Turkey (tr.fatih.sengul@gmail.com)
Harzallah H, Jurie F, Schmid C (2009). Combining efficient object localization and image classification. In 2009 IEEE 12th international conference on computer vision (pp. 237-244). IEEE. DOI: https://doi.org/10.1109/ICCV.2009.5459257
Zhao ZQ, Zheng P, Xu ST, Wu X (2019) Object detection with deep learning: A review. IEEE Trans Neural Netw Learn Syst 30(11):3212–3232 DOI: https://doi.org/10.1109/TNNLS.2018.2876865
Khurana K, Awasthi R (2013) Techniques for object recognition in images and multi-object detection. Int J Adv Res Comput Eng Technol (IJARCET) 2(4):1383–1388