Apriandy Angdresey; Indah Yessi Kairupan; Andre Gabriel Mongkareng

Web-Based Waste Detection Using YOLOv8 and Classification Performance Comparison: MobileNet and EfficientNet

PDF (1407KB), PP.1-18

Views: 0 Downloads: 0

Author(s)

Apriandy Angdresey ^1,* Indah Yessi Kairupan ¹ Andre Gabriel Mongkareng ¹

1. Department of Informatics Engineering, Universitas Katolik De La Salle Manado, 95000, Indonesia

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2025.06.01

Received: 11 Nov. 2024 / Revised: 20 Mar. 2025 / Accepted: 26 Aug. 2025 / Published: 8 Dec. 2025

Index Terms

Waste Detection, Object Classification, YOLOv8s Algorithm, MobileNetV2, EfficientNet

Abstract

Environmental pollution resulting from waste is a critical global challenge that significantly affects both the environment and public health, especially in countries like Indonesia. Effective waste management and recycling depend on accurately detecting and classifying different waste types. This study tackles this challenge by evaluating the YOLOv8s algorithm for object detection and conducting a comparative analysis of two mobile-optimized convolutional neural networks (CNNs), MobileNetV2 and EfficientNet, for waste classification. The YOLOv8s model established a promising baseline for detection, achieving a mean Average Precision (mAP@50) of 0.621 on the hold-out test set. MobileNetV2 proved to be the superior architecture in the classification task, attaining a higher accuracy of 94.4% compared to EfficientNet’s 87.8%. Additionally, MobileNetV2 demonstrated significantly greater computational efficiency, with a processing time of 229 ms per step, in contrast to EfficientNet’s 606 ms per step. These findings confirm that combining YOLOv8s for detection and MobileNetV2 for classification provides a robust and efficient pathway for developing automated waste management systems.

Cite This Paper

Apriandy Angdresey, Indah Yessi Kairupan, Andre Gabriel Mongkareng, "Web-Based Waste Detection Using YOLOv8 and Classification Performance Comparison: MobileNet and EfficientNet", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.17, No.6, pp. 1-18, 2025. DOI:10.5815/ijigsp.2025.06.01

Reference

[1]I. M. D. Adnyana, K. Mahendra, and S. Meesam Raza, “The Importance of Green Education in Indonesia: An Analysis of Opportunities and Challenges,” Education Specialist, vol. 1, pp. 61–68, Nov. 2023, doi: 10.59535/es.v1i2.168.
[2]I. Tyagi, R. R. Karri, N. M. Mubarak, and M. H. Dehghani, “Emerging pollutants in the aqueous solution,” in Sustainable Remediation Technologies for Emerging Pollutants in Aqueous Environment, 2023. doi: 10.1016/B978-0-443-18618-9.00010-3.
[3]G. Herrera-Franco, B. Merchán-Sanmartín, J. Caicedo-Potosí, J. B. Bitar, E. Berrezueta, and P. Carrión-Mero, “A systematic review of coastal zone integrated waste management for sustainability strategies,” 2024. doi: 10.1016/j.envres.2023.117968.
[4]P. Gao, Z. Han, and F. Wan, “Big data processing and application research,” in Proceedings - 2020 2nd International Conference on Artificial Intelligence and Advanced Manufacture, AIAM 2020, 2020. doi: 10.1109/AIAM50918.2020.00031.
[5]A. Cuzzocrea and E. Damiani, “Privacy-Preserving Big Data Exchange: Models, Issues, Future Research Directions,” in Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021, 2021. doi: 10.1109/BigData52589.2021.9671686.
[6]V. Wiley and T. Lucas, “Computer Vision and Image Processing: A Paper Review,” International Journal of Artificial Intelligence Research, vol. 2, no. 1, 2018, doi: 10.29099/ijair.v2i1.42.
[7]J. Chai, H. Zeng, A. Li, and E. W. T. Ngai, “Deep learning in computer vision: A critical review of emerging techniques and application scenarios,” Machine Learning with Applications, vol. 6, 2021, doi: 10.1016/j.mlwa.2021.100134.
[8]A. Ahmed, A. S. Imran, A. Manaf, Z. Kastrati, and S. M. Daudpota, “Enhancing wrist abnormality detection with YOLO: Analysis of state-of-the-art single-stage detection models,” Biomed Signal Process Control, vol. 93, 2024, doi: 10.1016/j.bspc.2024.106144.
[9]J. Da Wu and C. H. Chang, “Driver Drowsiness Detection and Alert System Development Using Object Detection,” Traitement du Signal, vol. 39, no. 2, 2022, doi: 10.18280/ts.390211.
[10]B. Ma et al., “Using an improved lightweight YOLOv8 model for real-time detection of multi-stage apple fruit in complex orchard environments,” Artificial Intelligence in Agriculture, vol. 11, 2024, doi: 10.1016/j.aiia.2024.02.001.
[11]Y. Wang et al., “VV-YOLO: A Vehicle View Object Detection Model Based on Improved YOLOv4,” Sensors, vol. 23, p. 3385, Nov. 2023, doi: 10.3390/s23073385.
[12]P. K. Samanta, A. Basuli, N. K. Rout, and G. Panda, “Improved Breast Cancer Detection from Ultrasound Images Using YOLOv8 Model,” in 2023 IEEE 3rd International Conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2023, 2023. doi: 10.1109/AESPC59761.2023.10390341.
[13]S. Kato, A. Oshita, T. Kubo, and M. Todai, “Classification of Steel Microstructure Image Using CNN,” in Lecture Notes on Data Engineering and Communications Technologies, vol. 189, 2024. doi: 10.1007/978-3-031-46970-1_6.
[14]A. Angdresey, L. Sitanayah, and E. Pantas, “Comparison of the Convolutional Neural Network Architectures for Traffic Object Classification,” in Proceedings - 2023 10th International Conference on Computer, Control, Informatics and its Applications: Exploring the Power of Data: Leveraging Information to Drive Digital Innovation, IC3INA 2023, 2023. doi: 10.1109/IC3INA60834.2023.10285791.
[15]R. Indraswari, R. Rokhana, and W. Herulambang, “Melanoma image classification based on MobileNetV2 network,” Procedia Comput Sci, vol. 197, pp. 198–207, 2022, doi: https://doi.org/10.1016/j.procs.2021.12.132.
[16]Q. Abbas, Y. Daadaa, U. Rashid, M. Z. Sajid, and M. E. A. Ibrahim, “HDR-EfficientNet: A Classification of Hypertensive and Diabetic Retinopathy Using Optimize EfficientNet Architecture,” Diagnostics, vol. 13, no. 20, 2023, doi: 10.3390/diagnostics13203236.
[17]M. Shao et al., “Comparison of EfficientNet-B0 and Res2Net Models for Distinguishing Malignant and Benign Pulmonary Nodules,” May 29, 2024. doi: 10.21203/rs.3.rs-4397415/v1.
[18]A. Angdresey, I. Y. Kairupan, and A. G. Mongkareng, “Leveraging Convolutional Neural Networks for Multiclass Waste Classification,” Journal of Applied Informatics and Computing, vol. 9, no. 4, pp. 1137–1145, Aug. 2025, doi: 10.30871/jaic.v9i4.9373.
[19]N. Sharma, R. Sharma, and N. Jindal, “Machine Learning and Deep Learning Applications-A Vision,” Global Transitions Proceedings, vol. 2, no. 1, 2021, doi: 10.1016/j.gltp.2021.01.004.
[20]I. H. Sarker, “Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions,” 2021. doi: 10.1007/s42979-021-00815-1.
[21]T. Diwan, G. Anirudh, and J. V. Tembhurne, “Object detection using YOLO: challenges, architectural successors, datasets and applications,” Multimed Tools Appl, vol. 82, no. 6, 2023, doi: 10.1007/s11042-022-13644-y.
[22]M. R. Sholahuddin et al., “Optimizing YOLOv8 for Real-Time CCTV Surveillance: A Trade-off Between Speed and Accuracy,” Jurnal Online Informatika, vol. 8, no. 2, 2023, doi: 10.15575/join.v8i2.1196.
[23]Y. Li, Q. Fan, H. Huang, Z. Han, and Q. Gu, “A Modified YOLOv8 Detection Network for UAV Aerial Image Recognition,” Drones, vol. 7, no. 5, 2023, doi: 10.3390/drones7050304.
[24]M. M. Krishna, M. Neelima, M. Harshali, and M. V. G. Rao, “Image classification using Deep learning,” International Journal of Engineering and Technology(UAE), vol. 7, 2018, doi: 10.55041/ijsrem27484.
[25]L. Alzubaidi et al., “Review of deep learning: concepts, CNN architectures, challenges, applications, future directions,” J Big Data, vol. 8, no. 1, Dec. 2021, doi: 10.1186/s40537-021-00444-8.
[26]P. Beleya, “Challenges in Enhancing Solid Waste Management towards Sustainable Environment: Local Council Perspectives,” Nov. 2019.
[27]A. Howard et al., “Searching for mobileNetV3,” Proceedings of the IEEE International Conference on Computer Vision, vol. 2019-October, pp. 1314–1324, Oct. 2019, doi: 10.1109/ICCV.2019.00140.
[28]M. Basheer Ahmed et al., “Deep Learning Approach to Recyclable Products Classification: Towards Sustainable Waste Management,” Sustainability, vol. 15, p. 11138, Nov. 2023, doi: 10.3390/su151411138.
[29]H. Abdu and M. H. Mohd Noor, “A Survey on Waste Detection and Classification Using Deep Learning,” IEEE Access, vol. PP, p. 1, Nov. 2022, doi: 10.1109/ACCESS.2022.3226682.
[30]G. Jocher, A. Chaurasia, and J. Qiu, “Ultralytics YOLOv8,” 2023. [Online]. Available: https://github.com/ultralytics/ultralytics

International Journal of Image, Graphics and Signal Processing (IJIGSP)