Real-time Fast Selection System with Object Recognition and TSP algorithms
DOI:
https://doi.org/10.18100/ijamec.1222732Keywords:
Food production, Object recognition, Computer vision, Seperating system, Travel salesman problemAbstract
The stage before the conversion of agricultural products into post-harvest consumer products is the process of separating the raw products into appropriate classes. Today, this difficult manual separating process is a process in which a large number of workers work at an intense pace on the product line and the workforce is intensively spent. Disruptions in separating as a result of carelessness cause product loss, loss of time and cost increases. In this study, as an alternative to manual separating processes, a real-time separating system, which detects the products in the factory band with object recognition methods and enables fast positioning of the separating tool on the products, works simultaneously with object recognition and traveling salesman problem algorithms has been created. In this way, a low-budget separating system is recommended for large selecting processes with a time- and cost-effective selecting model. In the study, the creation of a real-time fast separating system with the support of the traveling salesman algorithm, performance evaluation and research and findings on the fast separating model are presented.Downloads
References
Pala, F., Mennan, H., Çığ, F., Dilmen, H. (2018). ” Diyarbakır’da Buğday Ürününe Karışan Yabancı Ot Tohumlarının Belirlenmesi”, Türkiye Tarımsal Araştırmalar Dergisi, 5(3): 183-190.
Tursun, N., Kantarcı, Z., Seyithanoğlu, M. (2004). “Adıyaman ve Gaziantep bölgelerinde buğday ürününe karışan yabancı ot tohumlarının belirlenmesi”, Türkiye Herboloji Dergisi, 7(1): 1-12.
Tursun, N., Kantarcı, Z., Seyithanoğlu, M. (2006). “Kahramanmaraş’ta buğday ürününe karışan yabancı ot tohumlarının belirlenmesi”, Kahramanmaraş Sütçü İmam Üniversitesi Fen ve Mühendislik Dergisi, 9(2):110-115.
Özkil, M., Kara, A. (2006). “Trakya bölgesinde selektörden önce ve sonra buğday ürününe karışan yabancı ot tohumlarının ve yoğunluklarının belirlenmesi”, Trakya Üniversitesi Fen Bilimleri Dergisi, 7(1): 45-52.
Gökalp, Ö., Üremiş, İ. (2015). “Mardin’de buğday ürününe karışan yabancı ot tohumlarının belirlenmesi”, Mustafa Kemal Üniversitesi Ziraat Fakültesi Dergisi, 20(1): 23-30.
Baş, A., Karaca, M., Güncan, A. (2016). “Doğu Karadeniz Bölgesi’nde buğday ürününe karışan yabancı ot tohumlarının tespiti ve dağılışları”, Turkish Journal of Weed Science, 19(2): 49-60.
Şin, B., Kadıoğlu, İ., Kamışlı, B. (2016). “Tokat ilinde buğday ürünü içerisine karışan yabancı ot tohumlarının belirlenmesi”, Turkish Journal of Weed Science, 19(2): 28-37.
Asav, Ü., Kadıoğlu, İ. (2014). “Rusya Fedarasyonu’ndan Türkiye’ye ithal edilmek üzere Trabzon Limanı’na gelen buğdaylardaki yabancı ot tohumlarının belirlenmesi”, Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 4(4): 29-36.
Wastage and casualty rates of food products, Available: https://api.izto.org.tr/storage/Documents/original/pzhkKkWXfmkY2xYg.pdf
Jha, S.N., 2010. In Nondestructive Evaluation of Food Quality, 17, DOI 10.1007/978-3-642-15796-7_2,C_, Springer-Verlag Berlin Heidelberg.
Seçmen, Ö., Leblebici, E. (1987). “Yurdumuzun Zehirli Bitkileri”, Ege Üniversitesi Fen Fakültesi Baskı İşleri, İzmir.
Viola, P., Jones, M. (2004). "Robust real-time face detection," in Internation Journal of Computer Vision.
Kalinovskii, I.A., Spitsyn, V.G. (2015). “Compact Convolutional Neural Network Cascade for Face Detection”, Computer Science, p:375-387.
Bhavana, Naveen V.J., Kishore, K.K. (2019). “Comprehensive Analysis of Machine Learning Algorithms for Face Detection”, International Journal of Innovative Technology and Exploring Engineering, 8(10):2479-2482.
Dalal, N., Triggs, B. (2005). “Histograms of oriented gradients for
human detection”, In Computer Vision and Pattern Recogni-
tion(1):886–893.
Redmon, J., Divvala, S.K., Girshick, R.B., & Farhadi, A. (2016). You Only Look Once: Unified, Real-Time Object Detection. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 779-788.
Mutlu, M. , Özdem, K., Akcayol, M. A. (2022). “Derin Öğrenme ile Kuş Türü Sınıflandırma: Karşılaştırmalı Bir Çalışma” . Politeknik Dergisi , , 1-1.
Jiang, P., Ergu, D., Liu, F., Cai, Y., Ma, B. (2022).” A Review of Yolo Algorithm Developments”, Procedia Computer Science(199): 1066-1073.
Lee, Y., Kim, Y. (2020). “Comparison of CNN and YOLO for Object Detection”, Journal of the Semiconductor & Display Technology, 19(1): 85-92.
Kim, M., Lee, D.G., Kim, K.Y. (2015). “System Architecture for Real-Time Face Detection on Analog Video Camera”, International Journal of Distributed Sensor Networks 2015(5):1-11.
Yustiawati, R. et al., (2018). "Analyzing Of Different Features Using Haar Cascade Classifier," International Conference on Electrical Engineering and Computer Science (ICECOS):129-134
Vardin, H., Yılmaz, F.M. (2011). “Gıda Üretim Tesisleri Tasarımın Bileşenleri”, Harran Üniversitesi Ziraat Fakültesi Dergisi, 15(2):13-18.
Dahiya, C., Sangwan, S. (2018). “Literature Review on Travelling Salesman Problem”, Internetional Journal of Research, 5(16): 1152-1155.
Ahmed, B., Chouhan, S., Biswas, S. (2017). “Analysis of travelling salesman problem”, IOP Conference Series Materials Science and Engineering 263(4): 042085.
Vukmirovic, S., Pupavac, D. (2013). “The Travelling Salesman Problem in the Function of Transport Network”, Interdisciplinary Management Research(9): 325-334.
Osaba, E., Yang, X.S., Ser, J.D. (2020). “Chapter 9 - Traveling salesman problem: a perspective review of recent research and new results with bio-inspired metaheuristics”, Nature-Inspired Computation and Swarm Intelligence, 135-164
Twaróg, S., Szwarc, K., Wronka-Pośpiech, M., Dobrowolska, M., Urbanek, A. (2021). “Multiple probabilistic traveling salesman problem in the coordination of drug transportation-In the context of sustainability goals and Industry 4.0” PLoS One, 16(3):e0249077.
Gencel, C.A., Keçeci, B. (2019). “Traveling Salesman Problem with Hotel Selection: Comparative Study of the Alternative Mathematical Formulations”, Procedia Manufacturing(39): 1699-1708.
Gu, W., Liu, Y., Wei, L., Dong, B. (2015). “A Hybrid Optimization Algorithm for Travelling Salesman Problem Based on Geographical Information System for Logistics Distribution”, LISS 2014, 1641-1646.
Pezhhan, E., Mansoori, E. (2014). “A Biologically Inspired Solution for Fuzzy Travelling Salesman Problem”, Artificial Intelligence and Signal Processing. AISP 2013, 277-287.
Downloads
Published
Issue
Section
License
Copyright (c) 2023 International Journal of Applied Methods in Electronics and Computers
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.