Adaptive Multi-Scale Gaussian-Laplacian Pyramid with Gabor Filtering for Microplastics Detection
Received: 29 September 2025 | Revised: 16 November 2025 | Accepted: 21 November 2025 | Online: 9 February 2026
Corresponding author: Wahyono
Abstract
Microplastics, defined as plastic particles with characteristic dimensions smaller than 5 mm, have emerged as a major environmental pollutant, posing significant threats to marine ecosystems, wildlife, and human health. Accurate detection and classification of microplastics in environmental samples are therefore essential for monitoring their spatial distribution. This study proposes an adaptive Gaussian-Laplacian pyramid-based framework for multi-scale image decomposition to enhance microplastic detection in microscopic images. Unlike conventional methods that use a fixed number of pyramid levels, the proposed approach dynamically selects the most informative level for each image based on quantitative metrics such as variance, edge energy, and entropy. This adaptive selection ensures optimal feature extraction at the most relevant scale, improving detection accuracy for small and variably shaped microplastics. Detection is performed using a sliding window approach combined with a Support Vector Machine (SVM) classifier, followed by Non-Maximum Suppression (NMS) to eliminate duplicate detections. The system was evaluated on 574 microscopic images, achieving high detection sensitivity with an accuracy of 0.93 on Level 2 with a radius of 15 pixels. A comparative analysis with recent studies demonstrates that the proposed method offers superior scalability and balanced detection performance, particularly for small objects.
Keywords:
microplastic detection, Gaussian-Laplacian pyramid, multi-scale image decomposition, contrast enhancement, object detectionDownloads
References
M. N. Issac and B. Kandasubramanian, "Effect of microplastics in water and aquatic systems," Environmental Science and Pollution Research, vol. 28, no. 16, pp. 19544–19562, Apr. 2021. DOI: https://doi.org/10.1007/s11356-021-13184-2
X. Sun and X.-F. Sun, "An edge detection algorithm based upon the adaptive multi-directional anisotropic Gaussian filter and its applications," The Journal of Supercomputing, vol. 80, no. 11, pp. 15183–15214, July 2024. DOI: https://doi.org/10.1007/s11227-024-06044-6
A. H. Anik, S. Hossain, M. Alam, M. Binte Sultan, Md. T. Hasnine, and Md. M. Rahman, "Microplastics pollution: A comprehensive review on the sources, fates, effects, and potential remediation," Environmental Nanotechnology, Monitoring & Management, vol. 16, Dec. 2021, Art. no. 100530. DOI: https://doi.org/10.1016/j.enmm.2021.100530
V. Asha, G. M, M. L. Kolambkar, M. P, N. G. V, and A. Prasad, "Classification of Plastic Waste Products using Deep Learning," in 2024 1st International Conference on Cognitive, Green and Ubiquitous Computing (IC-CGU), Bhubaneswar, India, Mar. 2024, pp. 1–6. DOI: https://doi.org/10.1109/IC-CGU58078.2024.10530692
G. Abbas, U. Ahmed, and M. A. Ahmad, "Impact of Microplastics on Human Health: Risks, Diseases, and Affected Body Systems," Microplastics, vol. 4, no. 2, May 2025, Art. no. 23. DOI: https://doi.org/10.3390/microplastics4020023
S. Matavos-Aramyan, "Addressing the microplastic crisis: A multifaceted approach to removal and regulation," Environmental Advances, vol. 17, Oct. 2024, Art. no. 100579. DOI: https://doi.org/10.1016/j.envadv.2024.100579
M. E. Mihailov, A. V. Chiroșca, E. D. Pantea, and G. Chiroșca, "Machine Learning Approaches for Microplastic Pollution Analysis in Mytilus galloprovincialis in the Western Black Sea," Sustainability, vol. 17, no. 12, June 2025, Art. no. 5664. DOI: https://doi.org/10.3390/su17125664
P. Akkajit, M. E. E. Alahi, and A. Sukkuea, "Enhanced detection and classification of microplastics in marine environments using deep learning," Regional Studies in Marine Science, vol. 80, Dec. 2024, Art. no. 103880. DOI: https://doi.org/10.1016/j.rsma.2024.103880
Y. Yao, W. Xu, and H. Fan, "A Deep Learning Approach for Microplastic Segmentation in Microscopic Images," Toxics, vol. 13, no. 12, Nov. 2025, Art. no. 1018. DOI: https://doi.org/10.3390/toxics13121018
T. Saidani, "Deep Learning Approach: YOLOv5-based Custom Object Detection," Engineering, Technology & Applied Science Research, vol. 13, no. 6, pp. 12158–12163, Dec. 2023. DOI: https://doi.org/10.48084/etasr.6397
K. Han et al., "Innovative methods for microplastic characterization and detection: Deep learning supported by photoacoustic imaging and automated pre-processing data," Journal of Environmental Management, vol. 359, May 2024, Art. no. 120954. DOI: https://doi.org/10.1016/j.jenvman.2024.120954
T. Tanprawat, S. Laitrakun, P. Somnuake, and P. Opaprakasit, "Microplastic Spectral Classification Using Deep Learning with Denoising and Dimensionality Reduction," in 2024 9th International Conference on Business and Industrial Research (ICBIR), Bangkok, Thailand, May 2024, pp. 0811–0816. DOI: https://doi.org/10.1109/ICBIR61386.2024.10875756
S. Sundar, "A Novel Low-Cost Approach For Detection, Classification, and Quantification of Microplastic Pollution in Freshwater Ecosystems using IoT devices and Instance Segmentation," in 2022 IEEE MIT Undergraduate Research Technology Conference (URTC), Cambridge, MA, USA, Sept. 2022, pp. 1–5. DOI: https://doi.org/10.1109/URTC56832.2022.10002222
L. Wei, "Genetic Algorithm Optimization of Concrete Frame Structure Based on Improved Random Forest," in 2023 International Conference on Electronics and Devices, Computational Science (ICEDCS), Marseille, France, Sept. 2023, pp. 249–253. DOI: https://doi.org/10.1109/ICEDCS60513.2023.00051
Z. Chen et al., "A Hybrid MIR-spectrum Processing Algorithm for Microplastics Analysis," in 2024 7th International Conference on Electronics Technology (ICET), Chengdu, China, May 2024, pp. 1120–1125. DOI: https://doi.org/10.1109/ICET61945.2024.10672915
Microplastic Dataset Computer Vision Dataset. (2022),
Panats MP Project. [Online]. Available: https://universe.roboflow.com/panats-mp-project/microplastic-dataset.
M. M. Nanavati and M. Shah, "Implementation and Comparative Study of Pyramid-based Image Fusion Techniques for Lumbar Spine Images," Engineering, Technology & Applied Science Research, vol. 13, no. 4, pp. 11139–11145, Aug. 2023. DOI: https://doi.org/10.48084/etasr.5960
S. Galata, I. Walkington, T. Lane, K. Kiriakoulakis, and J. J. Dick, "Rapid detection of microfibres in environmental samples using open-source visual recognition models," Journal of Hazardous Materials, vol. 480, Dec. 2024, Art. no. 135956. DOI: https://doi.org/10.1016/j.jhazmat.2024.135956
M. Z. Bin Zahir Arju et al., "Deep-learning enabled rapid and low-cost detection of microplastics in consumer products following on-site extraction and image processing," RSC Advances, vol. 15, no. 14, pp. 10473–10483, 2025. DOI: https://doi.org/10.1039/D4RA07991D
X. Yu, S. Wu, X. Lu, and G. Gao, "Adaptive multiscale feature for object detection," Neurocomputing, vol. 449, pp. 146–158, Aug. 2021. DOI: https://doi.org/10.1016/j.neucom.2021.04.002
J. Zhang et al., "Small object intelligent detection method based on adaptive recursive feature pyramid," Heliyon, vol. 9, no. 7, July 2023, Art. no. e17730. DOI: https://doi.org/10.1016/j.heliyon.2023.e17730
G. Wen, L. Cheng, H. Yuan, and X. Li, "Surface Defect Detection Based on Adaptive Multi-Scale Feature Fusion," Sensors, vol. 25, no. 6, Mar. 2025, Art. no. 1720. DOI: https://doi.org/10.3390/s25061720
Downloads
How to Cite
License
Copyright (c) 2026 Ahmad Cahyono Adi, Wahyono
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
