Development of Automatic Object Detection and IoT for Garbage Pickup Assignment Problem
DOI: http://dx.doi.org/10.62527/joiv.8.2.2740
Abstract
Keywords
Full Text:
PDFReferences
P. S. Muttaqin, W. Margareta, and A. D. Zahira, “Green warehouse performance monitoring system design using analytical hierarchy process and supply chain operation reference,” Applied Engineering and Technology, vol. 1, no. 3, pp. 146–153, 2022, doi: 10.3176/aet.v1i1.687.
N. A. Habibi, A. Y. Ridwan, and E. B. Setyawan, “Determination of minimum trucks and routes used in the case of municipal solid waste transportation in Bandung City with greedy algoritm,” in IOP Conference Series: Materials Science and Engineering, IOP Publishing Ltd, Dec. 2020. doi: 10.1088/1757-899X/1007/1/012037.
A. Anton, N. F. Nissa, A. Janiati, N. Cahya, and P. Astuti, “Application of Deep Learning Using Convolutional Neural Network (CNN) Method For Women’s Skin Classification,” Scientific Journal of Informatics, vol. 8, no. 1, pp. 144–153, May 2021, doi: 10.15294/sji.v8i1.26888.
A. Duykuluoğlu, “The Significance of Artificial Neural Networks in Educational Research: A Summary of Research and Literature,” 2020.
D. Jaswal and K. P. Soman, “Image Classification Using Convolutional Neural Networks,” International Journal of Advancements in Research & Technology, vol. 3, no. 6, 2014, [Online]. Available: http://www.ijser.org
N. Nurtiwi, R. Ruliana, and Z. Rais, “Convolutional Neural Network (CNN) Method for Classification of Images by Age,” JINAV: Journal of Information and Visualization, vol. 3, no. 2, pp. 126–130, Dec. 2022, doi: 10.35877/454ri.jinav1481.
R. Prasath, M. Ramprasath Sr Assistant professor, Mv. Anand Professor, and S. Hariharan Professor, “Image Classification using Convolutional Neural Networks,” 2022. [Online]. Available: http://www.acadpubl.eu/hub/
P. Muttaqin and D. D. Damayanti, “Determination of Fleet Routes at PT. XYZ Uses Tabu Search Algorithm on Heterogeneous Fleet Vehicle Routing with Windows Time to Minimize Distance and Transportation Costs Based on Geographic Information Systems,” ICLS 2016, 2016.
IEEE ITSS, Institute of Transportation Engineers, and Institute of Electrical and Electronics Engineers, 2018 IEEE Intelligent Transportation Systems Conference : November 4-7, Maui, Hawaii.
E. Bayu Setyawan, A. Yunita, and S. Rasmaydiwa Sekarjatiningrum, “Development of Automatic Real Time Inventory Monitoring System using RFID Technology in Warehouse,” 2023. [Online]. Available: www.joiv.org/index.php/joiv
L. Heilig, E. Lalla-Ruiz, and S. Voß, “Multi-objective inter-terminal truck routing,” Transp Res E Logist Transp Rev, vol. 106, pp. 178–202, Oct. 2017, doi: 10.1016/j.tre.2017.07.008.
E. Panca Saputra and E. Panca, “Classification Using Artifical Neural Network Method in Protecting Credit Fitness,” Indonesian Journal of Artificial Intelligence and Data Mining (IJAIDM), vol. 3, no. 1, pp. 50–56, 2020, doi: 10.24014/ijaidm.v3i1.9442.
P. S. Muttaqin, W. Margareta, and A. D. Zahira, “Green warehouse performance monitoring system design using analytical hierarchy process and supply chain operation reference,” Applied Engineering and Technology, vol. 1, no. 3, pp. 146–153, 2022, doi: 10.3176/aet.v1i1.687.
Q. Xiao, F. Li, X. Ge, and X. Yu, “Research on scheduling optimization of internal trucks for inter-terminal transportation,” in Journal of Physics: Conference Series, Institute of Physics, 2022. doi: 10.1088/1742-6596/2277/1/012005.
A. R. Muhammad, H. P. Utomo, P. Hidayatullah, and N. Syakrani, “Early Stopping Effectiveness for YOLOv4,” Journal of Information Systems Engineering and Business Intelligence, vol. 8, no. 1, pp. 11–20, Apr. 2022, doi: 10.20473/jisebi.8.1.11-20.
H. Alsadeg et al., “Detection of Militia Object in Libya by Using YOLO Transfer Learning Info Artikel ABSTRAK,” Tahun, vol. 6, no. 1, 2020, [Online]. Available: http://http://jurnal.unmer.ac.id/index.php/jtmi
K. A. Baihaqi and Y. Cahyana, “Application of Convolution Neural Network Algorithm for Rice Type Detection Using Yolo v3,” 2021.
S. Kumar, D. Yadav, H. Gupta, O. P. Verma, I. A. Ansari, and C. W. Ahn, “A novel yolov3 algorithm-based deep learning approach for waste segregation: Towards smart waste management,” Electronics (Switzerland), vol. 10, no. 1, pp. 1–20, Jan. 2021, doi: 10.3390/electronics10010014.
N. Shah, L. Panigrahi, A. Patel, S. Tiwari, M. University, and K. J. Somaiya, “Classification and Segregation of Garbage for Recyclability Process,” International Journal of Science and Research, 2018, doi: 10.21275/SR20426184751.
M. Valente, H. Silva, J. M. L. P. Caldeira, V. N. G. J. Soares, and P. D. Gaspar, “Detection of waste containers using computer vision,” Applied System Innovation, vol. 2, no. 1, pp. 1–13, Mar. 2019, doi: 10.3390/asi2010011.
J. Parth M., 2020 International Conference for Emerging Technology (INCET) : Belgaum, India. Jun 5-7, 2020.
X. Li, M. Tian, S. Kong, L. Wu, and J. Yu, “A modified YOLOv3 detection method for vision-based water surface garbage capture robot,” Int J Adv Robot Syst, vol. 17, no. 3, May 2020, doi: 10.1177/1729881420932715.
G. Cavone, M. Dotoli, N. Epicoco, and C. Seatzu, “A decision making procedure for robust train rescheduling based on mixed integer linear programming and Data Envelopment Analysis,” Appl Math Model, vol. 52, pp. 255–273, 2017, doi: 10.1016/j.apm.2017.07.030.
S. Arshiya Mahaboobunisa, S. Gayathri, Y. Konda Babu, and V. Sasi Kumar, “A Novel Approach for Using Common Objects in Context Dataset (Coco) and Real Time Object Detection Using ML,” 2020. [Online]. Available: https://youtu.be/1LCb1PVqzeY
S. Rostianingsih, A. Setiawan, and C. I. Halim, “Instance Segmentation for Large, Multi-Channel Remote Sensing Imagery Using Mask-RCNN and a Mosaicking Approach,” 2020. [Online]. Available: www.sciencedirect.comwww.elsevier.com/locate/procedia1877-0509
O. L. F. de Carvalho et al., “Instance segmentation for large, multi-channel remote sensing imagery using mask-RCNN and a mosaicking approach,” Remote Sens (Basel), vol. 13, no. 1, pp. 1–24, Jan. 2021, doi: 10.3390/rs13010039.
A. P. Saputra, “Waste Object Detection and Classification using Deep Learning Algorithm: YOLOv4 and YOLOv4-tiny,” 2021.
Z. Zheng, P. Wang, W. Liu, J. Li, R. Ye, and D. Ren, “Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression,” Nov. 2019, [Online]. Available: http://arxiv.org/abs/1911.08287
Jain College of Engineering, Institute of Electrical and Electronics Engineers. Bangalore Section., and Institute of Electrical and Electronics Engineers, 2020 International Conference for Emerging Technology (INCET) : Belgaum, India. Jun 5-7, 2020.
E. B. Setyawan and D. Diah Damayanti, “Integrated railway timetable scheduling optimization model and rescheduling recovery optimization model: A systematic literature review,” 2018 5th International Conference on Industrial Engineering and Applications, ICIEA 2018, pp. 226–230, 2018, doi: 10.1109/IEA.2018.8387101.
A. Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao, “YOLOv4: Optimal Speed and Accuracy of Object Detection,” Apr. 2020, [Online]. Available: http://arxiv.org/abs/2004.10934