Smart Camera for Volcano Eruption Early Warning System Based on  Faster R-CNN and YOLO

Hasanur Mohammad Firdausi; Satryo Budi Utomo; Widjonarko Widjonarko

doi:10.21107/rekayasa.v18i1.27372

Smart Camera for Volcano Eruption Early Warning System Based on Faster R-CNN and YOLO

Hasanur Mohammad Firdausi, Satryo Budi Utomo, Widjonarko Widjonarko

Abstract

This research uses two object detection algorithms, Faster R-CNN with ResNet50 backbone and YOLOv5, to develop an intelligent camera system for monitoring volcanic activities. The models were trained and evaluated using CCTV footage from Mount Semeru, a region prone to volcanic eruptions. Key performance metrics such as Precision, Recall, and mean Average Precision (mAP) were used to evaluate the performance of both models. The high precision numbers for YOLOv5 and Faster R-CNN show they are good at avoiding false positives, which is essential for volcanic monitoring. YOLOv5 has a precision of 83.2%, while Faster R-CNN is 84%. However, recall shows a more significant difference between the two models. Faster R-CNN has a recall of 82%, meaning it is better at detecting all relevant volcanic activities, even if that means catching a few false positives. The variations in performance can be attributed to their respective designs. YOLOv5 is designed to achieve rapid, real-time detection by simultaneously predicting bounding boxes and class probabilities. This approach enhances speed but may slightly reduce recall. Faster R-CNN uses a two-stage process, tending to be more accurate but can be slower and less flexible across different IoU thresholds. Its higher recall means it catches more objects, contributing to its lower mAP@50-95 since it could struggle with overlapping or varying-sized objects.

Keywords

deep learning, faster R-CNN, dissaster, smart camera, YOLO

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References

Ahmed, K., Ghareh Mohammadi, F., Matus, M., Shenavarmasouleh, F., Pereira, L., Zisis, I., & Amini, M. H. (2021). Towards Real-Time House Detection in Aerial Images Using Faster Region-Based Convolutional Neural Network. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3994191

Al-Jawahry, H. M., Hussein, A. H. A., Sunil, G., Alkhafaji, M. A., & Pareek, P. K. (2023). Faster Region-Convolutional Neural Network with ResNet50 for Video Stream Object Detection. 2023 3rd International Conference on Mobile Networks and Wireless Communications (ICMNWC), 1–6. https://doi.org/10.1109/ICMNWC60182.2023.10435864

Anslam Sibi, S., Sivamohan, S., Prabhakaran, S., & Albin, S. B. (2024). The Resnet-50 Revolution: Leveraging Transfer Learning for Malaria Diagnosis. 2024 2nd International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA), 1–5. https://doi.org/10.1109/AIMLA59606.2024.10531528

Awaludin, L., Harjoko, A., & Sumiharto Raden. (2012). Pemrosesan Video Pendeteksi Kecepatan dan Ketinggian Aliran Lahar Dingin Pendukung Sistem Peringatan Dini. IJEIS, 2, 187–198.

Bappy, J. H., & Roy-Chowdhury, A. K. (2016). CNN based region proposals for efficient object detection. 2016 IEEE International Conference on Image Processing (ICIP), 3658–3662. https://doi.org/10.1109/ICIP.2016.7533042

Biswas, S., Nandy, A., Naskar, A. K., & Saw, R. (2024). Real time Gesture Recognition using Improved YOLOv5 Model. 2024 11th International Conference on Signal Processing and Integrated Networks (SPIN), 328–333. https://doi.org/10.1109/SPIN60856.2024.10511787

Cummins, P. R. (2017). Geohazards in Indonesia: Earth science for disaster risk reduction – introduction. Geological Society, London, Special Publications, 441(1), 1–7. https://doi.org/10.1144/SP441.11

Dong, P., & Wang, W. (2016). Better region proposals for pedestrian detection with R-CNN. 2016 Visual Communications and Image Processing (VCIP), 1–4. https://doi.org/10.1109/VCIP.2016.7805452

Hua, W., & Tong, Q. (2020). Research on Face Expression Detection Based on Improved Faster R-CNN. 2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), 1189–1193. https://doi.org/10.1109/ICAICA50127.2020.9182525

Jiang, P., Ergu, D., Liu, F., Cai, Y., & Ma, B. (2022). A Review of Yolo Algorithm Developments. Procedia Computer Science, 199, 1066–1073. https://doi.org/https://doi.org/10.1016/j.procs.2022.01.135

Kido, S., Hirano, Y., & Hashimoto, N. (2018). Detection and classification of lung abnormalities by use of convolutional neural network (CNN) and regions with CNN features (R-CNN). 2018 International Workshop on Advanced Image Technology (IWAIT), 1–4. https://doi.org/10.1109/IWAIT.2018.8369798

Kusumasari, B. (2019). Natural Hazards Governance in Indonesia. In Oxford Research Encyclopedia of Natural Hazard Science. Oxford University Press. https://doi.org/10.1093/acrefore/9780199389407.013.234

Lin, Q., Zhao, J., Tong, Q., Zhang, G., Yuan, Z., & Fu, G. (2019). Cropping Region Proposal Network Based Framework for Efficient Object Detection on Large Scale Remote Sensing Images. 2019 IEEE International Conference on Multimedia and Expo (ICME), 1534–1539. https://doi.org/10.1109/ICME.2019.00265

Liu, Y. (2018). An Improved Faster R-CNN for Object Detection. 2018 11th International Symposium on Computational Intelligence and Design (ISCID), 02, 119–123. https://doi.org/10.1109/ISCID.2018.10128

Lu, Z., Ding, L., Wang, Z., Dong, L., & Guo, Z. (2023). Road Condition Detection Based on Deep Learning YOLOv5 Network. 2023 IEEE 3rd International Conference on Electronic Technology, Communication and Information (ICETCI), 497–501. https://doi.org/10.1109/ICETCI57876.2023.10176545

M, P., Sreekumar, S., & S, A. (2022). Detection of Covid-19 from the Chest X-Ray Images: A Comparison Study between CNN and Resnet-50. 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon), 1–7. https://doi.org/10.1109/MysuruCon55714.2022.9972488

Mansoor, A., Porras, A. R., & Linguraru, M. G. (2019). Region Proposal Networks with Contextual Selective Attention for Real-Time Organ Detection. 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), 1193–1196. https://doi.org/10.1109/ISBI.2019.8759480

Martinez, A., & Hudayana, B. (2022). From Transmigrasi to Relokasi. Archipel, 104, 57–74. https://doi.org/10.4000/archipel.3004

Prasad, J. V. D., Chandra, M. K., Akash, J., & Vivek, K. (2023). Coconut Tree Detection in Coastal Areas with Fast-RCNN Using Resnet-50. 2023 Global Conference on Information Technologies and Communications (GCITC), 1–4. https://doi.org/10.1109/GCITC60406.2023.10426612

Praveen, T. N. V. S., Sivathmika, D., Jahnavi, G., & Bolledu, J. (2023). An In-depth Exploration of ResNet-50 for Complex Emotion Recognition to Unraveling Emotional States. 2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT), 1–5. https://doi.org/10.1109/InCACCT57535.2023.10141774

Priyadharshini, G., & Judie Dolly, D. R. (2023). Comparative Investigations on Tomato Leaf Disease Detection and Classification Using CNN, R-CNN, Fast R-CNN and Faster R-CNN. 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS), 1, 1540–1545. https://doi.org/10.1109/ICACCS57279.2023.10112860

Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You Only Look Once: Unified, Real-Time Object Detection. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 779–788. https://doi.org/10.1109/CVPR.2016.91

Ren, S., He, K., Girshick, R., & Sun, J. (2017). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6), 1137–1149. https://doi.org/10.1109/TPAMI.2016.2577031

Tahir, H., Shahbaz Khan, M., & Owais Tariq, M. (2021). Performance Analysis and Comparison of Faster R-CNN, Mask R-CNN and ResNet50 for the Detection and Counting of Vehicles. 2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), 587–594. https://doi.org/10.1109/ICCCIS51004.2021.9397079

Upadhyay, D., Manwal, M., Kukreja, V., & Sharma, R. (2024). A Fine-Tuned YOLOv5 and Exception Model for Oral Cancer Detection. 2024 5th International Conference for Emerging Technology (INCET), 1–5. https://doi.org/10.1109/INCET61516.2024.10592942

Vuyyuru, L., Nalluri, S., Vempatapu, J., & Thopuri, R. babu. (2023). Air Pollution Detection using Resnet-50. 2023 7th International Conference on Computing Methodologies and Communication (ICCMC), 845–849. https://doi.org/10.1109/ICCMC56507.2023.10083837

Wang, Y., Liu, J., Yu, S., Wang, K., Han, Z., & Tang, Y. (2021). Underwater Object Detection based on YOLO-v3 network. 2021 IEEE International Conference on Unmanned Systems (ICUS), 571–575. https://doi.org/10.1109/ICUS52573.2021.9641489

Yanagisawa, H., Yamashita, T., & Watanabe, H. (2018). A study on object detection method from manga images using CNN. 2018 International Workshop on Advanced Image Technology (IWAIT), 1–4. https://doi.org/10.1109/IWAIT.2018.8369633

Yang, H., Liu, P., Hu, Y., & Fu, J. (2021). Research on underwater object recognition based on YOLOv3. Microsystem Technologies, 27. https://doi.org/10.1007/s00542-019-04694-8

Yuhana, U. L., Edo, G., & Syarif, H. (2023). Enhancement of Blurred Indonesian License Plate Number Identification Using Multi-Scale Information CNN. 2023 3rd International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON), 1–6. https://doi.org/10.1109/SMARTGENCON60755.2023.10442912

Zamanidoost, Y., Alami-Chentoufi, N., Ould-Bachir, T., & Martel, S. (2023). Efficient Region Proposal Extraction of Small Lung Nodules Using Enhanced VGG16 Network Model. 2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS), 483–488. https://doi.org/10.1109/CBMS58004.2023.00266