Deep Learning Based Garbage Detection for Autonomous Garbage Collection Vehicles

Öz

With the rapid technological advances and the increasing human population, the need for more production has emerged and consumption has increased accordingly. As a result of this increased consumption, more garbage has been generated. Environmental pollution caused by these garbages emerges as a problem that people have to overcome both in Turkey and in the world. Many studies have been conducted to overcome this problem. Especially today, with the development of autonomous vehicles and artificial intelligence, the solutions using these technologies have increased. In this study, a new data set was created for autonomous garbage collection vehicles and a model was proposed in which these vehicles can be used. The data set was prepared with images of garbage with paper cups, which is one of the most polluting garbage, taken in different places, and images consisting of different garbage without paper cups. Paper cups were detected from these images with pre-trained Squenzenet, VGG-19 and GoogLeNet convolutional neural networks. The performance rate of the SquenzeNet, GoogLeNet and Vgg-19 networks used in the study was found as 97.77%, 96.44% 94.66%, respectively.

Anahtar Kelimeler

• Transfer learning
• Garbage detection
• Garbage dataset.

Kaynakça

1. Akanksha, Gupta, M., & Tripathi, M. M. (2021). Smart robot for collection and segregation of garbage. Proceedings of International Conference on Innovative Practices in Technology and Management, ICIPTM 2021, 169–173. https://doi.org/10.1109/ICIPTM52218.2021.9388369
2. Bircanoglu, C., Atay, M., Beser, F., Genc, O., & Kizrak, M. A. (2018). RecycleNet: Intelligent Waste Sorting Using Deep Neural Networks. 2018 IEEE (SMC) International Conference on Innovations in Intelligent Systems and Applications, INISTA 2018. https://doi.org/10.1109/INISTA.2018.8466276
3. Chiba, S., & Sasaoka, H. (2021). Basic Study for Transfer Learning for Autonomous Driving in Car Race of Model Car. 6th International Conference on Business and Industrial Research, ICBIR 2021 - Proceedings, 138–141. https://doi.org/10.1109/ICBIR52339.2021.9465856
4. Cireşan, D. C., Meier, U., & Schmidhuber, J. (2012). Transfer learning for Latin and Chinese characters with deep neural networks. In Proceedings of the International Joint Conference on Neural Networks (pp. 1–6). IEEE. https://doi.org/10.1109/IJCNN.2012.6252544
5. Coşkun, F., & Gülleroğlu, H. D. (2021). Geçmişten Günümüze Yapay Zekanın Gelişimi ve Eğitim Alanında Kullanılması. Ankara Universitesi Egitim Bilimleri Fakultesi Dergisi, 1–20. https://doi.org/10.30964/auebfd.916220
6. Deepak, S., Medicine, P. A.-C. in B. and, & 2020, undefined. (n.d.). Retrieval of brain MRI with tumor using contrastive loss based similarity on GoogLeNet encodings. Elsevier. Retrieved from https://www.sciencedirect.com/science/article/pii/S0010482520303243
7. Elmas, B. (2021). Identifying species of trees through bark images by convolutional neural networks with transfer learning method. Journal of the Faculty of Engineering and Architecture of Gazi University, 36(3), 1253–1269. https://doi.org/10.17341/gazimmfd.689038
8. Fu, W., Xue, B., Gao, X., Computing, M. Z.-A. S., & 2021, undefined. (n.d.). Transductive transfer learning based Genetic Programming for balanced and unbalanced document classification using different types of features. Elsevier. Retrieved from https://www.sciencedirect.com/science/article/pii/S1568494621000958

9. Guo, Y., Chen, J., Du, Q., Hengel, A. Van Den, Shi, Q., networks, M. T.-N., & 2020, undefined. (2020). Multi-way backpropagation for training compact deep neural networks. Elsevier, 126, 250–261. https://doi.org/10.1016/j.neunet.2020.03.001
10. Jaloli, M., Choudhary, D., & Cescon, M. (2020). Neurological Status Classification Using Convolutional Neural Network. IFAC-PapersOnLine, 53(5), 409–414. https://doi.org/10.1016/j.ifacol.2021.04.193
11. Kang, M. S., Kim, P. K., & Lim, K. T. (2020). A Simple and fast method to detect garbage dumping using pedestrian attribute. 2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020. https://doi.org/10.1109/ICCE-ASIA49877.2020.9276940
12. Khairandish, M., Sharma, M., Jain, V., IRBM, J. C.-, & 2021, undefined. (2021). A Hybrid CNN-SVM Threshold Segmentation Approach for Tumor Detection and Classification of MRI Brain Images. Elsevier. https://doi.org/10.1016/j.irbm.2021.06.003
13. Lee, M., Lee, J., Kim, J., … B. K.-2019 I. S., & 2019, undefined. (n.d.). The Sparsity and Activation Analysis of Compressed CNN Networks in a HW CNN Accelerator Model. Ieeexplore.Ieee.Org. Retrieved from https://ieeexplore.ieee.org/abstract/document/9027643/
14. Liu, J., Zhang, Q., Li, X., Li, G., Liu, Z., Xie, Y., … 2021, undefined. (n.d.). Transfer learning-based strategies for fault diagnosis in building energy systems. Elsevier. Retrieved from https://www.sciencedirect.com/science/article/pii/S0378778821005405
15. Lloyd, K., Marshall, D., Moore, S. C., & Rosin, P. L. (2016). Detecting Violent and Abnormal Crowd activity using Temporal Analysis of Grey Level Co-occurrence Matrix (GLCM) Based Texture Measures. https://doi.org/10.1007/s00138-017-0830-x
16. Mahiba, C., Measurement, A. J.-, & 2019, undefined. (n.d.). Severity analysis of diabetic retinopathy in retinal images using hybrid structure descriptor and modified CNNs. Elsevier. Retrieved from https://www.sciencedirect.com/science/article/pii/S0263224118311771
17. Min, S., Lee, B., & Yoon, S. (2017). Deep learning in bioinformatics. Briefings in Bioinformatics, 18(5), 851–869. https://doi.org/10.1093/BIB/BBW068
18. Polsinelli, M., Cinque, L., & Placidi, G. (2020). A light CNN for detecting COVID-19 from CT scans of the chest. Pattern Recognition Letters, 140, 95–100. https://doi.org/10.1016/J.PATREC.2020.10.001
19. Rao, P. P., Rao, S. P., & Ranjan, R. (2020). Deep Learning Based Smart Garbage Monitoring System. MPCIT 2020 - Proceedings: IEEE 3rd International Conference on “Multimedia Processing, Communication and Information Technology,” 77–81. https://doi.org/10.1109/MPCIT51588.2020.9350390
20. Siavashi, J., Najafi, A., Ebadi, M., Fuel, M. S.-, & 2022, undefined. (n.d.). A CNN-based approach for upscaling multiphase flow in digital sandstones. Elsevier. Retrieved from https://www.sciencedirect.com/science/article/pii/S0016236121019232
21. Singh, D. (2021). Polyth-Net: Classification of Polythene Bags for Garbage Segregation Using Deep Learning. 2021 International Conference on Sustainable Energy and Future Electric Transportation, SeFet 2021. https://doi.org/10.1109/SEFET48154.2021.9375766
22. Ucar, F., hypotheses, D. K.-M., & 2020, undefined. (n.d.). COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images. Elsevier. Retrieved from https://www.sciencedirect.com/science/article/pii/S0306987720307702
23. Xiao, X., Xiao, W., Zhang, D., Zhang, B., Hu, G., Security, Q. L.-C. &, & 2021, undefined. (n.d.). Phishing websites detection via CNN and Multi-Head Self-Attention on imbalanced datasets. Elsevier. Retrieved from https://www.sciencedirect.com/science/article/pii/S0167404821001966
24. Zhou, J., Ren, K., Wan, M., Cheng, B., Gu, G., Optik, Q. C.-, & 2021, undefined. (n.d.). An Infrared and Visible Image Fusion Method Based on VGG-19 Network. Elsevier. Retrieved from https://www.sciencedirect.com/science/article/pii/S0030402621016363