Abstract
Recycling waste sorting with high accuracy has become a significant area of research in recent years due to its direct effect on environment and economy. Computer-aided approaches are able to provide effective performances in recycling processes which consist of big data sizes. In this work, a deep learning (DL) based image analysis approach has been improved by using Python programming language and the YOLOv8x DL algorithm to optimize the recycling processes. From the simulation results, it can be concluded that DL-based approaches are able to provide high accuracy in image recognition and can successfully be used in processing of big data sets.
Supporting Institution
This work has been supported by Scientific and Technological Research Council of Turkey (TUBITAK) under TUBITAK-2209 A Program
Project Number
Application number: 1919B012200459
References
- [1] Haque IR, Neubert J. Deep learning approaches to biomedical image segmentation. Inform in Medic Unlocked 2020; 18:1- 12.
- [2] Kodipalli A, Guha S, Dasar S, İsmail T. An inception-ResNet deep learning approach to classify tumours in the ovary as benign and malignant. Expert Syst 2022;e13215:1-11.
- [3] Momeny M, Jahanbakhshi A, Jafarnezhad K, Zhang YD. Accurate classification of cherry fruit using deep CNN based on hybrid pooling approach. Postharvest Biology Tech 2020;166: 1-9.
- [4] Zekiwos M, Bruck A. Deep learning-based image processing for cotton leaf disease and pest diagnosis. J Elect Comp Eng 2021;2021:1-10,
- [5] Huixian J. The analysis of plants image recognition based on deep learning and artificial neural network. IEEE Access 2020;8:68828-68841.
- [6] Wang P, Fan E, Wang P. Comparative analysis of image classification algorithms based on traditional machine learning and deep learning. Pattern Recog Letters 2021;141:61-67.
- [7] Shen L, Lang B, Song Z. DS-YOLOv8-based object detection method for remote sensing images. IEEE Access 2023;11:125122-125137.
- [8] Kingma DP, Ba J. Adam: A method for stochastic optimization. In Proceedings of 3rd International Conference for Learning Representations, 2015, pp. 1-9.
Abstract
Recycling waste sorting with high accuracy has become a significant area of research in recent years due to its direct effect on environment and economy. Computer-aided approaches are able to provide effective performances in recycling processes which consist of big data sizes. In this work, a deep learning (DL) based image analysis approach has been improved by using Python programming language and the YOLOv8x DL algorithm to optimize the recycling processes. From the simulation results, it can be concluded that DL-based approaches are able to provide high accuracy in image recognition and can successfully be used in processing of big data sets.
Project Number
Application number: 1919B012200459
References
- [1] Haque IR, Neubert J. Deep learning approaches to biomedical image segmentation. Inform in Medic Unlocked 2020; 18:1- 12.
- [2] Kodipalli A, Guha S, Dasar S, İsmail T. An inception-ResNet deep learning approach to classify tumours in the ovary as benign and malignant. Expert Syst 2022;e13215:1-11.
- [3] Momeny M, Jahanbakhshi A, Jafarnezhad K, Zhang YD. Accurate classification of cherry fruit using deep CNN based on hybrid pooling approach. Postharvest Biology Tech 2020;166: 1-9.
- [4] Zekiwos M, Bruck A. Deep learning-based image processing for cotton leaf disease and pest diagnosis. J Elect Comp Eng 2021;2021:1-10,
- [5] Huixian J. The analysis of plants image recognition based on deep learning and artificial neural network. IEEE Access 2020;8:68828-68841.
- [6] Wang P, Fan E, Wang P. Comparative analysis of image classification algorithms based on traditional machine learning and deep learning. Pattern Recog Letters 2021;141:61-67.
- [7] Shen L, Lang B, Song Z. DS-YOLOv8-based object detection method for remote sensing images. IEEE Access 2023;11:125122-125137.
- [8] Kingma DP, Ba J. Adam: A method for stochastic optimization. In Proceedings of 3rd International Conference for Learning Representations, 2015, pp. 1-9.
Details
| Primary Language | English |
|---|---|
| Subjects | Signal Processing |
| Journal Section | Research Articles |
| Authors | |
| Project Number | Application number: 1919B012200459 |
| Publication Date | June 30, 2024 |
| Submission Date | May 3, 2024 |
| Acceptance Date | May 10, 2024 |
| Published in Issue | Year 2024 Volume: 2 Issue: 1 |
