DSRO based data annotation with improved EfficientNet for forest fire detection using image processing in IoT environment
Abstract
The increasing risk of forest fires demands sophisticated detection systems in order to mitigate the environment effectively. The technology under consideration enhances real-time monitoring and reaction by functioning inside an Internet of Things (IoT) architecture. Even though Artificial Intelligence (AI) algorithms have improved fire detection systems, they are quite expensive and energy-intensive due to their high computing needs. With the use of creative methods for data augmentation and optimization as well as a shared feature extraction module, this research study offers a thorough fire detection model using an improved EfficientNet that tackles these issues. Three technical components are creatively combined in the realm of forest fire detection by this study. The first stage is the use of diagonal swap of random (DSRO) data annotation, which makes use of spatial connections in the data to improve the model’s understanding of complex aspects that are essential for precisely identifying possible fire breakouts. By adding a shared feature extraction module across three functions, the second stage solves difficulties in feature extraction and target identification. This greatly increases the model’s performance in complicated forest scenes while reducing false positives and false negatives. The third and final stage focuses on improving the EfficientNet model’s capacity for accurate forest fire categorization. When taken as a whole, these technical components upon creative combination improve the existing technology in forest fire detection and provide a thorough and practical strategy for reducing environmental hazards. For the purpose of hyperparameter tuning in the EfficientNet for the classification of forest fires, an improved Harris Hawks optimization (HHO) is used. By using the Cauchy mutation approach with adaptive weight, HHO expands the search space, boosts population diversity, and improves overall exploration. By including the sine-cosine algorithm (SCA) into the optimization process, the likelihood of local extremum occurrences is decreased. The proposed strategy is successful compared to other existing models, as shown by the experimental findings that show an improvement of 5% in accuracy compared to the standard existing model, and an improvement of 2% compared to EfficientNet model in detecting forest fire.
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1. Dampage U, Bandaranayake L, Wanasinghe R, et al. Forest fire detection system using wireless sensor networks and machine learning. Scientific Reports. 2022, 12(1). doi: 10.1038/s41598-021-03882-9
2. Guede-Fernández F, Martins L, de Almeida RV, et al. A Deep Learning Based Object Identification System for Forest Fire Detection. Fire. 2021, 4(4): 75. doi: 10.3390/fire4040075
3. Khan A, Hassan B, Khan S, et al. DeepFire: A Novel Dataset and Deep Transfer Learning Benchmark for Forest Fire Detection. Kim H jin, ed. Mobile Information Systems. 2022, 2022: 1-14. doi: 10.1155/2022/5358359
4. Senthil Kumar G, Ramana K, Madana Mohana R, et al. An Effective Bootstrapping Framework for Web Services Discovery Using Trigram Approach. Hakak S, ed. Mobile Information Systems. 2022, 2022: 1-12. doi: 10.1155/2022/2373596
5. Singh H, Shukla A, Kumar S. IoT based Forest Fire Detection System in Cloud Paradigm. IOP Conference Series: Materials Science and Engineering. 2021, 1022(1): 012068. doi: 10.1088/1757-899x/1022/1/012068
6. Gaitan NC, Hojbota P. Forest Fire Detection System using LoRa Technology. International Journal of Advanced Computer Science and Applications. 2020, 11(5). doi: 10.14569/ijacsa.2020.0110503
7. Avula SB, Badri SJ, Reddy P G. A Novel Forest Fire Detection System Using Fuzzy Entropy Optimized Thresholding and STN-based CNN. 2020 International Conference on COMmunication Systems & NETworkS (COMSNETS). Published online January 2020. doi: 10.1109/comsnets48256.2020.9027347
8. Barmpoutis P, Papaioannou P, Dimitropoulos K, et al. A Review on Early Forest Fire Detection Systems Using Optical Remote Sensing. Sensors. 2020, 20(22): 6442. doi: 10.3390/s20226442
9. Sudhakar S, Vijayakumar V, Sathiya Kumar C, et al. Unmanned Aerial Vehicle (UAV) based Forest Fire Detection and monitoring for reducing false alarms in forest-fires. Computer Communications. 2020, 149: 1-16. doi: 10.1016/j.comcom.2019.10.007
10. Benzekri W, El A, Moussaoui O, et al. Early Forest Fire Detection System using Wireless Sensor Network and Deep Learning. International Journal of Advanced Computer Science and Applications. 2020, 11(5). doi: 10.14569/ijacsa.2020.0110564
11. Jiao Z, Zhang Y, Mu L, et al. A YOLOv3-based Learning Strategy for Real-time UAV-based Forest Fire Detection. 2020 Chinese Control And Decision Conference (CCDC). Published online August 2020. doi: 10.1109/ccdc49329.2020.9163816
12. Xue Z, Lin H, Wang F. A Small Target Forest Fire Detection Model Based on YOLOv5 Improvement. Forests. 2022, 13(8): 1332. doi: 10.3390/f13081332
13. Abid F. A Survey of Machine Learning Algorithms Based Forest Fires Prediction and Detection Systems. Fire Technology. 2020, 57(2): 559-590. doi: 10.1007/s10694-020-01056-z
14. Kasturi K, Reddy P V, Rao N A, Vinod S. A review of architecture and applications for Internet of Things. Advances in Natural and Applied Sciences. 2016, 10(9): 261-267.
15. Kasturi K, SriMourya K, Mahendra I, Maheshwari V. Gesture Recognition Based Device Control Using MEMS Accelerometer. International Journal of Engineering Science, Advanced Computing and Bio-Technology. 2017, 8(4). doi: 10.26674/ijesacbt/2017/49248
16. Kasturi K, Dharanikota R, Rehman K, Meyyappan S, Kommineni A. Low-Cost, IOT-Based Child Safety Monitoring Robot with User-Friendly Mobile App. Smart Sensors Measurement and Instrumentation . Lecture Notes in Electrical Engineering, vol 957. Springer, Singapore. doi:10.1007/978-981-19-6913-3_35
17. Kizilkaya B, Ever E, Yatbaz HY, et al. An Effective Forest Fire Detection Framework Using Heterogeneous Wireless Multimedia Sensor Networks. ACM Transactions on Multimedia Computing, Communications, and Applications. 2022, 18(2): 1-21. doi: 10.1145/3473037
18. El abbassi MA, Jilbab A, Bourouhou A. Efficient Forest Fire Detection System Based on Data Fusion Applied in Wireless Sensor Networks. International Journal on Electrical Engineering and Informatics. 2020, 12(1): 1-18. doi: 10.15676/ijeei.2020.12.1.1
19. Ya’acob N, Syamirza Mohd Najib M, Tajudin N, et al. Image Processing Based Forest Fire Detection using Infrared Camera. Journal of Physics: Conference Series. 2021, 1768(1): 012014. doi: 10.1088/1742-6596/1768/1/012014
20. Goyal S, Shagill MD, Kaur A, et al. A YOLO based Technique for Early Forest Fire Detection. International Journal of Innovative Technology and Exploring Engineering. 2020, 9(6): 1357-1362. doi: 10.35940/ijitee.f4106.049620
21. Abdusalomov AB, Islam BMS, Nasimov R, et al. An Improved Forest Fire Detection Method Based on the Detectron2 Model and a Deep Learning Approach. Sensors. 2023, 23(3): 1512. doi: 10.3390/s23031512
22. Lin J, Lin H, Wang F. A Semi-Supervised Method for Real-Time Forest Fire Detection Algorithm Based on Adaptively Spatial Feature Fusion. Forests. 2023, 14(2): 361. doi: 10.3390/f14020361
23. Avazov K, Hyun AE, Sami S AA, et al. Forest Fire Detection and Notification Method Based on AI and IoT Approaches. Future Internet. 2023, 15(2): 61. doi: 10.3390/fi15020061
24. Huang J, He Z, Guan Y, et al. Real-Time Forest Fire Detection by Ensemble Lightweight YOLOX-L and Defogging Method. Sensors. 2023, 23(4): 1894. doi: 10.3390/s23041894
25. Zhang L, Wang M, Ding Y, et al. MS-FRCNN: A Multi-Scale Faster RCNN Model for Small Target Forest Fire Detection. Forests. 2023, 14(3): 616. doi: 10.3390/f14030616
26. Lu X, Li B, Yue Y, et al. Grid R-CNN. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, 15–20 June 2019; pp. 7363–7372. doi: 10.1109/CVPR.2019.00754
27. Kaur P, Kaur K, Singh K, et al. Early Forest Fire Detection Using a Protocol for Energy-Efficient Clustering with Weighted-Based Optimization in Wireless Sensor Networks. Applied Sciences. 2023, 13(5): 3048. doi: 10.3390/app13053048
28. Almasoud AS. Intelligent Deep Learning Enabled Wild Forest Fire Detection System. Computer Systems Science and Engineering. 2023, 44(2): 1485-1498. doi: 10.32604/csse.2023.025190
29. Prasanna KR, Mathana JM, Ramya TA, et al. LoRa network based high performance forest fire detection system. Materials Today: Proceedings. 2023, 80: 1951-1955. doi: 10.1016/j.matpr.2021.05.656
30. Vikram R, Sinha D. A multimodal framework for Forest fire detection and monitoring. Multimedia Tools and Applications. 2022, 82(7): 9819-9842. doi: 10.1007/s11042-022-13043-3
31. Chen G, Zhou H, Li Z, et al. Multi-Scale Forest Fire Recognition Model Based on Improved YOLOv5s. Forests. 2023, 14(2): 315. doi: 10.3390/f14020315
32. Kong SG, Jin D, Li S, et al. Fast fire flame detection in surveillance video using logistic regression and temporal smoothing. Fire Safety Journal. 2016, 79: 37-43. doi: 10.1016/j.firesaf.2015.11.015
33. Douce GK, Moorhead DJ, Bargeron CT. Forestry Images. org: High-resolution image archive and web-available image system. J. For. Sci. 2001, 47, 77-79.
34. Cazzolato MT, Avalhais L, Chino D, et al. A compilation of datasets from emergency situations for fire and smoke analysis. In: Proceedings of the Brazilian Symposium on Databases-SBBD, Uberlandia, Brazil, 4-7 October 2017; pp. 213-223.
35. Chino DYT, Avalhais LPS, Rodrigues JF, et al. BoWFire: Detection of Fire in Still Images by Integrating Pixel Color and Texture Analysis. 2015 28th SIBGRAPI Conference on Graphics, Patterns and Images. Published online August 2015. doi: 10.1109/sibgrapi.2015.19
36. Kucuk G, Kosucu B, Yavas A, Baydere S. FireSense: Forest Fire Prediction and Detection System using Wireless Sensor Networks. In: Proceedings of the 4th IEEE/ACM International Conference on Distributed Computing in Sensor Systems (DCOSS’08), Santorini, Greece, 11-14 June 2008.
37. Li S, Yan Q, Liu P. An Efficient Fire Detection Method Based on Multiscale Feature Extraction, Implicit Deep Supervision and Channel Attention Mechanism. IEEE Transactions on Image Processing. 2020, 29: 8467-8475. doi: 10.1109/tip.2020.3016431
38. LabelImg. Available online: https://github.com/heartexlabs/labelImg (accessed on 5 December 2023).
39. Labelme. Available online: https://github.com/wkentaro/labelme (accessed on 5 December 2023).
40. Tan M, Le Q. Efficientnet: Rethinking model scaling for convolutional neural networks. In: Proceedings of the 36th International Conference on Machine Learning, Long Beach, CA, USA, 10–15 June 2019; pp. 6105-6114.
41. Liu Y, Cao B. A Novel Ant Colony Optimization Algorithm With Levy Flight. IEEE Access. 2020, 8: 67205-67213. doi: 10.1109/access.2020.2985498
42. Hussain K, Neggaz N, Zhu M, Houssein E. An efficient hybrid sine-cosine Harris hawks optimization for low and high-dimensional feature selection. Expert Systems with Applications. 2021, 176:114778. doi: https:// doi.org/ 10.1016/j.eswa.2021.114778
43. Shukla P, Aluvalu R, Gite S, et al. Computer Vision. De Gruyter Frontiers in Computational Intelligence. Published online February 20, 2023. doi: 10.1515/9783110756722
44. Goud BH, Shankar TN, Sah B, et al. Energy optimization in path arbitrary wireless sensor network. Expert Systems. Published online March 16, 2023. doi: 10.1111/exsy.13282
45. Li Y. Forest fire smoke recognition and detection based on EfficientNet. IEEE Conference on Telecommunications, Optics and Computer Science (TOCS), Dalian, China, 18 January 2023. doi: 10.1109/TOCS56154.2022.100160286028
DOI: https://doi.org/10.32629/jai.v7i4.1088
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