Klasifikasi Sampah di Saluran Air Menggunakan Algortima CNN
DOI:
https://doi.org/10.56705/ijodas.v3i2.33Keywords:
cnn, teknologi, artificial intelegence, sampah, deep learningAbstract
Tingginya populasi manusia turut menyumbangkan peningkatan jumlah sampah, sehingga dibutuhkan sebuah sistem yang membantu manusia mengklasifikasikan sampah. Perkembangan teknologi yang dirasakan hampir di semua aspek kehidupan termasuk pada pengembangan lingkungan. Dengan teknologi yang ada diharapkan bisa membantu meringankan tugas manusia dan meningkatkan efektifitas penggunaan waktu. Convolutional Neural Network (CNN) merupakan sebuah sistem pengolahan objek dengan pengenalan citra. Dan dengan menggunakan teknik CNN atau Convolutional Neural Network yang banyak digunakan untuk mengenali suatu objek dan diharapkan dapat mempermudah kerja manusia serta menghemat waktu yang digunakan.
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References
C. Xianbao, Q. Guihua, J. Yu, and Z. Zhaomin, “An improved small object detection method based on Yolo V3,” Pattern Anal. Appl., vol. 24, no. 3, pp. 1347–1355, 2021, doi: 10.1007/s10044-021-00989-7.
M. B. Bejiga, A. Zeggada, A. Nouffidj, and F. Melgani, “A convolutional neural network approach for assisting avalanche search and rescue operations with UAV imagery,” Remote Sens., vol. 9, no. 2, 2017, doi: 10.3390/rs9020100.
Y. Liu, P. Sun, N. M. Wergeles, and Y. Shang, “A survey and performance evaluation of deep learning methods for small object detection,” Expert Syst. Appl., 2021, doi: 10.1016/j.eswa.2021.114602.
Y. Liu, P. Sun, N. Wergeles, and Y. Shang, “A survey and performance evaluation of deep learning methods for small object detection,” Expert Syst. Appl., vol. 172, no. April 2020, p. 114602, 2021, doi: 10.1016/j.eswa.2021.114602.
K. Židek, A. Hosovsky, J. Piteľ, and S. Bednár, “Recognition of Assembly Parts by Convolutional Neural Networks,” Lect. Notes Mech. Eng., pp. 281–289, 2019, doi: 10.1007/978-3-319-99353-9_30.
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2016-Decem, pp. 779–788, 2016, doi: 10.1109/CVPR.2016.91.
R. Girshick, “Fast R-CNN,” Proc. IEEE Int. Conf. Comput. Vis., vol. 2015 Inter, pp. 1440–1448, 2015, doi: 10.1109/ICCV.2015.169.
C. Cao et al., “An Improved Faster R-CNN for Small Object Detection,” IEEE Access, 2019, doi: 10.1109/access.2019.2932731.
J. Noh, W. Bae, W. Lee, J. Seo, and G. Kim, “Better to follow, follow to be better: Towards precise supervision of feature super-resolution for small object detection,” Proc. IEEE Int. Conf. Comput. Vis., vol. 2019-Octob, pp. 9724–9733, 2019, doi: 10.1109/ICCV.2019.00982.
S. M. A. Bashir and Y. Wang, “Small object detection in remote sensing images with residual feature aggregation-based super-resolution and object detector network,” Remote Sens., vol. 13, no. 9, 2021, doi: 10.3390/rs13091854.
Z. Zhu, D. Liang, S. Zhang, X. Huang, B. Li, and S. Hu, “Traffic-Sign Detection and Classification in the Wild,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2016-Decem, pp. 2110–2118, 2016, doi: 10.1109/CVPR.2016.232.
Y. Liu et al., “Detecting Cancer Metastases on Gigapixel Pathology Images,” pp. 1–13, 2017, [Online]. Available: http://arxiv.org/abs/1703.02442.
Y. Liu, F. Yang, and P. Hu, “Small-Object Detection in UAV-Captured Images via Multi-Branch Parallel Feature Pyramid Networks,” IEEE Access, 2020, doi: 10.1109/access.2020.3014910.
O. Russakovsky et al., “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis., vol. 115, no. 3, pp. 211–252, 2015, doi: 10.1007/s11263-015-0816-y.
H. Azis, F. Tangguh Admojo, and E. Susanti, “Analisis Perbandingan Performa Metode Klasifikasi pada Dataset Multiclass Citra Busur Panah,” Techno.Com, vol. 19, no. 3, pp. 286–294, 2020.
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