Artificial intelligence-powered graph neural network-YOLO framework for real-time detection of environmental hazards in sustainable cities
Abstract
The current detection systems have limited real-time potential, have poor spatial relationship modeling, and accuracy of multi-hazard recognition, and hence the current challenges to sustainable development of cities are unprecedented due to the environmental degradation in cities. This study presents a new hybrid architecture of the application of Graph Neural Networks (GNN) and You Only Look Once version 8 (YOLOv8) to solve the issue of wholesome real-time identification of environmental hazards in smart cities. The proposed GNN-YOLOv8 model uses spatial-temporal graph convolutional layers to model the environmental sensor-networks and establishes the benefits of the developed visual hazard detection using advanced object detection abilities. The pipeline used includes three stages, which are constructing a graph based on heterogeneous sensor data, extracting features with the help of attention-based GNN modules, and classifying hazards with changed YOLOv8 and adaptable anchor mechanisms. The one-way ANOVA statistical analysis indicated that the results showed their significance in the difference of performance between hazard categories. The framework delivered real time processing rates of 67.3 frames per second and a latency of 14.8 milliseconds and could therefore be deployed to constrained resource edge computing devices. The study provides a scalable interpretable framework of sustainable city environmental surveillance that has an impressive contribution to the development of smart city infrastructure and climate change management meta compliances.
Full text article
References
[1] Wu Z, Pan S, Chen F, Long G, Zhang C, Yu PS. A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems. 2020 Mar 24;32(1):4-24. https://doi.org/10.1109/TNNLS.2020.2978386
[2] Scarselli F, Gori M, Tsoi AC, Hagenbuchner M, Monfardini G. The graph neural network model. IEEE transactions on neural networks. 2008 Dec 9;20(1):61-80. https://doi.org/10.1109/TNN.2008.2005605
[3] Corso G, Stark H, Jegelka S, Jaakkola T, Barzilay R. Graph neural networks. Nature Reviews Methods Primers. 2024 Mar 7;4(1):17. https://doi.org/10.1038/s43586-024-00294-7
[4] Xu K, Hu W, Leskovec J, Jegelka S. How powerful are graph neural networks?. arXiv preprint arXiv:1810.00826. 2018 Oct 1.
[5] Veličković P. Everything is connected: Graph neural networks. Current Opinion in Structural Biology. 2023 Apr 1;79:102538. https://doi.org/10.1016/j.sbi.2023.102538
[6] Wu L, Cui P, Pei J, Zhao L, Guo X. Graph neural networks: foundation, frontiers and applications. InProceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining 2022 Aug 14 (pp. 4840-4841). https://doi.org/10.1145/3534678.3542609
[7] Liu Z, Zhou J. Introduction to graph neural networks. Springer Nature; 2022 May 31. https://doi.org/10.1007/978-981-16-6054-2_17
[8] Shchur O, Mumme M, Bojchevski A, Günnemann S. Pitfalls of graph neural network evaluation. arXiv preprint arXiv:1811.05868. 2018 Nov 14.
[9] Zhu R, Zhao K, Yang H, Lin W, Zhou C, Ai B, Li Y, Zhou J. Aligraph: A comprehensive graph neural network platform. arXiv preprint arXiv:1902.08730. 2019 Feb 23. https://doi.org/10.14778/3352063.3352127
[10] Ying Z, Bourgeois D, You J, Zitnik M, Leskovec J. Gnnexplainer: Generating explanations for graph neural networks. Advances in neural information processing systems. 2019;32.
[11] Liu M, Gao H, Ji S. Towards deeper graph neural networks. InProceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining 2020 Aug 23 (pp. 338-348). https://doi.org/10.1145/3394486.3403076
[12] Dwivedi VP, Joshi CK, Luu AT, Laurent T, Bengio Y, Bresson X. Benchmarking graph neural networks. Journal of Machine Learning Research. 2023;24(43):1-48.
[13] Luan S, Hua C, Lu Q, Zhu J, Zhao M, Zhang S, Chang XW, Precup D. Revisiting heterophily for graph neural networks. Advances in neural information processing systems. 2022 Dec 6;35:1362-75.
[14] Rusch TK, Bronstein MM, Mishra S. A survey on oversmoothing in graph neural networks. arXiv preprint arXiv:2303.10993. 2023 Mar 20.
[15] Zheng X, Wang Y, Liu Y, Li M, Zhang M, Jin D, Yu PS, Pan S. Graph neural networks for graphs with heterophily: A survey. arXiv preprint arXiv:2202.07082. 2022 Feb 14.
[16] Fan W, Ma Y, Li Q, He Y, Zhao E, Tang J, Yin D. Graph neural networks for social recommendation. InThe world wide web conference 2019 May 13 (pp. 417-426). https://doi.org/10.1145/3308558.3313488
[17] Zhu J, Rossi RA, Rao A, Mai T, Lipka N, Ahmed NK, Koutra D. Graph neural networks with heterophily. InProceedings of the AAAI conference on artificial intelligence 2021 May 18 (Vol. 35, No. 12, pp. 11168-11176). https://doi.org/10.1609/aaai.v35i12.17332
[18] Reiser P, Neubert M, Eberhard A, Torresi L, Zhou C, Shao C, Metni H, van Hoesel C, Schopmans H, Sommer T, Friederich P. Graph neural networks for materials science and chemistry. Communications Materials. 2022 Nov 26;3(1):93. https://doi.org/10.1038/s43246-022-00315-6
[19] Agarwal C, Queen O, Lakkaraju H, Zitnik M. Evaluating explainability for graph neural networks. Scientific Data. 2023 Mar 18;10(1):144. https://doi.org/10.1038/s41597-023-01974-x
[20] Scarselli F, Gori M, Tsoi AC, Hagenbuchner M, Monfardini G. Computational capabilities of graph neural networks. IEEE Transactions on Neural Networks. 2008 Dec 9;20(1):81-102. https://doi.org/10.1109/TNN.2008.2005141
[21] You J, Ying R, Leskovec J. Position-aware graph neural networks. InInternational conference on machine learning 2019 May 24 (pp. 7134-7143). PMLR.
[22] Xhonneux LP, Qu M, Tang J. Continuous graph neural networks. InInternational conference on machine learning 2020 Nov 21 (pp. 10432-10441). PMLR.
[23] Wang X, Zhang M. How powerful are spectral graph neural networks. InInternational conference on machine learning 2022 Jun 28 (pp. 23341-23362). PMLR.
[24] Bessadok A, Mahjoub MA, Rekik I. Graph neural networks in network neuroscience. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2022 Sep 26;45(5):5833-48. https://doi.org/10.1109/TPAMI.2022.3209686
[25] Gao C, Zheng Y, Li N, Li Y, Qin Y, Piao J, Quan Y, Chang J, Jin D, He X, Li Y. A survey of graph neural networks for recommender systems: Challenges, methods, and directions. ACM Transactions on Recommender Systems. 2023 Mar 3;1(1):1-51. https://doi.org/10.1145/3568022
[26] Wu L, Chen Y, Shen K, Guo X, Gao H, Li S, Pei J, Long B. Graph neural networks for natural language processing: A survey. Foundations and Trends in Machine Learning. 2023 Jan 25;16(2):119-328. https://doi.org/10.1561/2200000096
[27] Liao W, Bak-Jensen B, Pillai JR, Wang Y, Wang Y. A review of graph neural networks and their applications in power systems. Journal of Modern Power Systems and Clean Energy. 2021 Aug 20;10(2):345-60. https://doi.org/10.35833/MPCE.2021.000058
[28] Jiang P, Ergu D, Liu F, Cai Y, Ma B. A Review of Yolo algorithm developments. Procedia computer science. 2022 Jan 1;199:1066-73. https://doi.org/10.1016/j.procs.2022.01.135
[29] Terven J, Córdova-Esparza DM, Romero-González JA. A comprehensive review of yolo architectures in computer vision: From yolov1 to yolov8 and yolo-nas. Machine learning and knowledge extraction. 2023 Nov 20;5(4):1680-716. https://doi.org/10.3390/make5040083
[30] Hussain M. YOLO-v1 to YOLO-v8, the rise of YOLO and its complementary nature toward digital manufacturing and industrial defect detection. Machines. 2023 Jun 23;11(7):677. https://doi.org/10.3390/machines11070677
[31] Hussain M. Yolov1 to v8: Unveiling each variant-a comprehensive review of yolo. IEEE access. 2024 Mar 19;12:42816-33. https://doi.org/10.1109/ACCESS.2024.3378568
[32] Chen W, Huang H, Peng S, Zhou C, Zhang C. YOLO-face: a real-time face detector. The Visual Computer. 2021 Apr;37(4):805-13. https://doi.org/10.1007/s00371-020-01831-7
[33] Zhou Y. A YOLO-NL object detector for real-time detection. Expert Systems with Applications. 2024 Mar 15;238:122256. https://doi.org/10.1016/j.eswa.2023.122256
[34] Vijayakumar A, Vairavasundaram S. Yolo-based object detection models: A review and its applications. Multimedia Tools and Applications. 2024 Oct;83(35):83535-74. https://doi.org/10.1007/s11042-024-18872-y
[35] Oreski G. YOLO* C-Adding context improves YOLO performance. Neurocomputing. 2023 Oct 28;555:126655. https://doi.org/10.1016/j.neucom.2023.126655
[36] Gündüz MŞ, Işık G. A new YOLO-based method for real-time crowd detection from video and performance analysis of YOLO models. Journal of Real-Time Image Processing. 2023 Feb;20(1):5. https://doi.org/10.1007/s11554-023-01276-w
[37] Ali ML, Zhang Z. The YOLO framework: A comprehensive review of evolution, applications, and benchmarks in object detection. Computers. 2024 Dec 14;13(12):336. https://doi.org/10.3390/computers13120336
[38] Ragab MG, Abdulkadir SJ, Muneer A, Alqushaibi A, Sumiea EH, Qureshi R, Al-Selwi SM, Alhussian H. A comprehensive systematic review of YOLO for medical object detection (2018 to 2023). IEEE Access. 2024 Apr 10;12:57815-36. https://doi.org/10.1109/ACCESS.2024.3386826
[39] Lan W, Dang J, Wang Y, Wang S. Pedestrian detection based on YOLO network model. In2018 IEEE international conference on mechatronics and automation (ICMA) 2018 Aug 5 (pp. 1547-1551). IEEE. https://doi.org/10.1109/ICMA.2018.8484698
[40] Tao J, Wang H, Zhang X, Li X, Yang H. An object detection system based on YOLO in traffic scene. In2017 6th International conference on computer science and network technology (ICCSNT) 2017 Oct 21 (pp. 315-319). IEEE. https://doi.org/10.1109/ICCSNT.2017.8343709
[41] Huang Y, Yan Q, Li Y, Chen Y, Wang X, Gao L, Tang Z. A YOLO-based table detection method. In2019 International Conference on Document Analysis and Recognition (ICDAR) 2019 Sep 20 (pp. 813-818). IEEE. https://doi.org/10.1109/ICDAR.2019.00135
[42] Prinzi F, Insalaco M, Orlando A, Gaglio S, Vitabile S. A yolo-based model for breast cancer detection in mammograms. Cognitive Computation. 2024 Jan;16(1):107-20. https://doi.org/10.1007/s12559-023-10189-6
[43] Ahmad T, Ma Y, Yahya M, Ahmad B, Nazir S, Haq AU. Object detection through modified YOLO neural network. Scientific Programming. 2020;2020(1):8403262. https://doi.org/10.1155/2020/8403262
[44] Badgujar CM, Poulose A, Gan H. Agricultural object detection with You Only Look Once (YOLO) Algorithm: A bibliometric and systematic literature review. Computers and Electronics in Agriculture. 2024 Aug 1;223:109090. https://doi.org/10.1016/j.compag.2024.109090
[45] Zhou J, Zhang B, Yuan X, Lian C, Ji L, Zhang Q, Yue J. YOLO-CIR: The network based on YOLO and ConvNeXt for infrared object detection. Infrared Physics & Technology. 2023 Jun 1;131:104703. https://doi.org/10.1016/j.infrared.2023.104703
[46] Jana AP, Biswas A. YOLO based Detection and Classification of Objects in video records. In2018 3rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) 2018 May 18 (pp. 2448-2452). IEEE. https://doi.org/10.1109/RTEICT42901.2018.9012375
[47] Su P, Han H, Liu M, Yang T, Liu S. MOD-YOLO: Rethinking the YOLO architecture at the level of feature information and applying it to crack detection. Expert Systems with Applications. 2024 Mar 1;237:121346. https://doi.org/10.1016/j.eswa.2023.121346
[48] Kang M, Ting CM, Ting FF, Phan RC. ASF-YOLO: A novel YOLO model with attentional scale sequence fusion for cell instance segmentation. Image and Vision Computing. 2024 Jul 1;147:105057. https://doi.org/10.1016/j.imavis.2024.105057
[49] Yu Z, Huang H, Chen W, Su Y, Liu Y, Wang X. Yolo-facev2: A scale and occlusion aware face detector. Pattern Recognition. 2024 Nov 1;155:110714. https://doi.org/10.1016/j.patcog.2024.110714
[50] Chandana RK, Ramachandra AC. Real time object detection system with YOLO and CNN models: A review. arXiv Prepr. arXiv2208. 2022 Jul 17;773.
[51] Tian Y, Yang G, Wang Z, Wang H, Li E, Liang Z. Apple detection during different growth stages in orchards using the improved YOLO-V3 model. Computers and electronics in agriculture. 2019 Feb 1;157:417-26. https://doi.org/10.1016/j.compag.2019.01.012
[52] Sirisha U, Praveen SP, Srinivasu PN, Barsocchi P, Bhoi AK. Statistical analysis of design aspects of various YOLO-based deep learning models for object detection. International Journal of Computational Intelligence Systems. 2023 Aug 2;16(1):126. https://doi.org/10.1007/s44196-023-00302-w
Authors
Copyright (c) 2025 International Journal of Applied Resilience and Sustainability (IJARS) 
This work is licensed under a Creative Commons Attribution 4.0 International License.