Comparative Performance of Yolov8 and Ssd-mobilenet Algorithms for Road Damage Detection in Mobile Applications

Authors

  • Arie Satia Dharma Fakultas Informatika dan Teknik Elektro, Program Studi Sarjana Informatika, Institut Teknologi Del, Laguboti, Indonesia http://orcid.org/0000-0002-6129-3869
  • Chantika Nadya Serebella Pardosi Fakultas Informatika dan Teknik Elektro, Program Studi Sarjana Informatika, Institut Teknologi Del, Laguboti, Indonesia
  • Zan Peter Silaen Fakultas Informatika dan Teknik Elektro, Program Studi Sarjana Informatika, Institut Teknologi Del, Laguboti, Indonesia

DOI:

10.33395/sinkron.v9i3.15008

Keywords:

YOLOv8, SSD-MobileNet, object detection, road damage, mobile application

Abstract

Road damage is a serious issue that can impede traffic and increase the risk of accidents in any area. Fast and accurate detection and classification of road damage are crucial for efficient maintenance and repair. Considering the ease of access, the implementation of this detection can be done using a mobile application. This study aims to compare the performance of two object detection algorithms, YOLOv8 and SSD-MobileNet, in detecting and classifying road damage in mobile application. Evaluation is conducted using accuracy, speed, and memory utilization, and classification of road damage into six categories namely block cracks, alligator cracks, transverse cracks, edge cracks, patches, and potholes using a confusion matrix. The results show that YOLOv8 has an overall accuracy of 86.4%, a speed of 0.5 ms, and consumes 0.41 GB of RAM. SSD-MobileNet shows an overall accuracy of 91.1%, speed 0.7 ms, and consumes 0.14 GB of RAM. The comparison indicates that YOLOv8 excels in detection speed, while SSD-MobileNet is more higher accuracy and efficient in memory. This study is limited to a performance measurement approach for YOLOv8 and SSD-MobileNet algorithms in a mobile-based road defect detection context. Its contribution lies in the trade-off between accuracy, speed, and the memory required to implement the models in limited devices. In future research is recommended to explore model with pruning to reduce memory usage.

GS Cited Analysis

Downloads

Download data is not yet available.

References

Sirisha, U., Praveen, S. P., Srinivasu, P. N., Barsocchi, P., & Bhoi, A. K. (2023). Statistical analysis of design aspects of various YOLO-based deep learning models for object detection. International Journal of Computational Intelligence Systems, 16(1), 1–29. https://doi.org/10.1007/s44196-023-00302-w

Drantantiyas, N. D. G., Yulita, W., Ridwan, N. T., Ramadhani, U. A., Kesuma, R. I., Rakhman, A. Z., Bagaskara, R., Miranto, A., & Mufidah, Z. (2023). Performasi deteksi jumlah manusia menggunakan YOLOv8. JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer), 5(2), 63–68. https://doi.org/10.26905/jasiek.v5i2.11605

Carranza-García, M., Torres-Mateo, J., Lara-Benítez, P., & García-Gutiérrez, J. (2021). On the performance of one-stage and two-stage object detectors in autonomous vehicles using camera data. Remote Sensing, 13(1), 1–23. https://doi.org/10.3390/rs13010089

Falah, K., Gustiana, M., & Ungkawa, U. (2021). Karakteristik metode MobileNet-SSD dengan pre-trained model MobileNet untuk objek bergerak. Jurnal Nasional Institut Teknologi Nasional, 5(2), 45–55. https://doi.org/10.1234/journal.5678

Pakpahan, B., & Dewi, I. C. (2021). Pendeteksian lubang pada jalanan menggunakan metode SSD-MobileNet. Indonesian Journal of Electronics and Instrumentation Systems (IJEIS), 11(2), 213–222. https://doi.org/10.22146/ijeis.60157

Choudhari, V., Phadtare, M., & Karad, V. (2021). Comparison between YOLO and SSD MobileNet for object detection in a surveillance drone. International Journal of Scientific Research and Engineering Management, 5(10), 0–5. https://doi.org/10.13140/RG.2.2.34029.51688

Sabina, N. (2022). Object detection using YOLO and MobileNet SSD: A comparative study. International Journal, 11(6), 134–138.

Toyip Setiawan, F. S., & Winayati. (2021). Identifikasi jenis-jenis kerusakan jalan (perkerasan lentur). Jurnal, 6(1), 69–77.

Kim, J. A., Sung, J. Y., & Park, S. H. (2020). Comparison of Faster-RCNN, YOLO, and SSD for real-time vehicle type recognition. In 2020 IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia) (pp. 8–11). IEEE. https://doi.org/10.1109/ICCE-Asia49877.2020.9277040

Nugroho, P. A., Fenriana, I., & Arijanto, R. (2020). Implementasi deep learning menggunakan convolutional neural network (CNN) pada ekspresi manusia. Algor, 2(1), 12–21.

Lou, H., et al. (2023). DC-YOLOv8: Small-size object detection algorithm based on camera sensor. Electronics, 12(10), 1–14. https://doi.org/10.3390/electronics12102323

Redmon, J., & Farhadi, A. (2017). YOLO9000: Better, faster, stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 6517–6525.

Redmon, J., & Farhadi, A. (2018). YOLOv3: An incremental improvement. arXiv. https://arxiv.org/abs/1804.02767

Wang, C., Bochkovskiy, A., & Liao, H. M. (2020). CSPNet: A new backbone that can enhance learning capability of CNN. arXiv. https://arxiv.org/abs/1911.11929

Iyer, R., Ringe, P. S., Iyer, R. V., & Bhensdadiya, K. P. (2021). Comparison of YOLOv3, YOLOv5s and MobileNet-SSD V2 for real-time mask detection. International Journal of Research in Engineering and Technology, 8(7), 1156–1160. https://www.researchgate.net/publication/353211011

Indra, D., Herman, H., & Budi, F. S. (2023). Implementasi sistem penghitung kendaraan otomatis berbasis computer vision. Komputika Jurnal Sistem Komputer, 12(1), 53–62. https://doi.org/10.34010/komputika.v12i1.9082

Kim, Y.-H., & Kim, Y. L. (2020). Comparison of CNN and YOLO for object detection. Journal of Semiconductor Display Technology, 19(1), 85–92.

Murthy, C. B., Hashmi, M. F., & Keskar, A. G. (2021). Optimized MobileNet + SSD: A real-time pedestrian detection on a low-end edge device. International Journal of Multimedia Information Retrieval, 10(3), 171–184. https://doi.org/10.1007/s13735-021-00212-7

Ulhaq, M. R. D., Zaidan, M. A., & Firdaus, D. (2023). Pengenalan ekspresi wajah secara real-time menggunakan metode SSD MobileNet berbasis Android. Jurnal Teknologi Informatika (JOTI), 5(1), 48–52. https://doi.org/10.37802/joti.v5i1.387

Krstinić, D., Braović, M., Šerić, L., & Božić-Štulić, D. (2020). Multi-label classifier performance evaluation with confusion matrix. Computer Science & Information Technology (CS & IT), 10(8), 1–14. https://doi.org/10.5121/csit.2020.100801

Sun, C., Zhang, H., Qin, S., Qin, J., Shi, Y., & Wen, Q. (2020). DroidPDF: The obfuscation resilient packer detection framework for Android apps. IEEE Access, 8, 167460–167474. https://doi.org/10.1109/ACCESS.2020.3022477

Downloads


Crossmark Updates

How to Cite

Dharma, A. S., Pardosi, C. N. S. ., & Silaen, Z. P. . (2025). Comparative Performance of Yolov8 and Ssd-mobilenet Algorithms for Road Damage Detection in Mobile Applications . Sinkron : Jurnal Dan Penelitian Teknik Informatika, 9(3), 1159-1169. https://doi.org/10.33395/sinkron.v9i3.15008

Issue

Vol. 9 No. 3 (2025): Article Research July 2025

Section

Articles

License

Copyright (c) 2025 Arie Satia Dharma, Chantika Nadya Serebella Pardosi, Zan Peter Silaen

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Crossref
Scopus
Google Scholar
Europe PMC