A Computer Vision-Based System for Metal Sheet Pick Counting
Source abstract
Abstract
Inventory counting is crucial to manufacturing industries in terms of inventory management, production, and procurement planning. Many companies currently require workers to manually count and track the status of materials, which are repetitive and non-value-added activities but incur significant costs to the companies as well as mental fatigue to the employees. This research aims to develop a computer vision system that can automate the material counting activity without applying any marker on the material. The type of material of interest is metal sheet, whose shape is simple, a large rectangular shape, yet difficult to detect. The use of computer vision technology can reduce the costs incurred from the loss of high-value materials, eliminate repetitive work requirements for skilled labor, and reduce human error. A computer vision system is proposed and tested on a metal sheet picking process for multiple metal sheet stacks in the storage area by using one video camera. Our results show that the proposed computer vision system can count the metal sheet picks under a real situation with a precision of 97.83% and a recall of 100%.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Industrial Vision Systems and Defect Detection · Advanced Neural Network Applications · Image and Object Detection Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (34)
Show source-provided references
1. S. Axsa¨ter, Inventory Control, New York: Springer, vol. 225, 2015.
2. Industry 4.0: Architecture and equipment revolution10.32604/cmc.2020.012587 ↗
3. Accurate multi-scale feature fusion CNN for time series classification in smart factory10.32604/cmc.2020.011108 ↗
4. An image edge detection algorithm based on genetic fuzzy
5. YOLOv2PD: An efficient pedestrian detection algorithm using improved YOLOv2 model10.32604/cmc.2021.018781 ↗
6. A critical review on computer vision and artificial intelligence in food industry10.1016/j.jafr.2020.100033 ↗
7. Meat quality evaluation based on computer vision technique: A review
8. Applications of machine vision in pharmaceutical technology: A review10.1016/j.ejps.2021.105717 ↗
9. A scientometric analysis and critical review of computer vision applications for construction10.1016/j.autcon.2019.102947 ↗
10. Object detection for smart factory processes by machine learning10.1016/j.procs.2021.04.009 ↗
11. A novel algorithm for defect extraction and classification of mobile phone screen based on machine vision10.1016/j.cie.2020.106530 ↗
12. A computer vision-based model for automatic motion time study10.32604/cmc.2022.030418 ↗
13. A method for counting and classifying aphids using computer vision10.1016/j.compag.2019.105200 ↗
14. Automatic live fingerlings counting using computer vision10.1016/j.compag.2019.105015 ↗
15. An automatic counting system for transparent pelagic fish eggs based on computer vision10.1016/j.aquaeng.2015.05.001 ↗
16. Computer vision-based counting model for dense steel pipe on construction sites10.1061/(ASCE)CO.1943-7862.0002217 ↗
17. A handy open-source application based on computer vision and machine learning algorithms to count and classify microplastics10.3390/w13152104 ↗
18. Computer vision techniques in manufacturing
19. Y. E. Wang, G. Y. Wei and D. Brooks, “Benchmarking TPU, GPU, and CPU platforms for deep learning,” 2019. [Online]. Available: http://arxiv.org/abs/1907.10701
20. The OpenCV library
21. SSDMNV2: A real time DNN-based face mask detection system using single shot multibox detector and MobileNetV210.1016/j.scs.2020.102692 ↗
22. The algorithm development for operation of a computer vision system via the OpenCV library10.1016/j.procs.2020.02.193 ↗
23. Application of attitude tracking algorithm for face recognition based on OpenCV in the intelligent door lock10.1016/j.comcom.2020.02.003 ↗
24. Deep learning based face detection and identification of criminal suspects10.32604/cmc.2023.032715 ↗
25. T. Lin, “LabelImg,” 2015. [Online]. Available: https://github.com/tzutalin/labelImg
26. M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen et al., “TensorFlow: Large-scale machine learning on heterogeneous distributed systems,” 2016. [Online]. Available: http://arxiv.org/abs/1603.04467
27. A survey on transfer learning10.1109/TKDE.2009.191 ↗
28. Mobilenet-SSDv2: An improved object detection model for embedded systems
29. Learning similarity with cosine similarity ensemble10.1016/j.ins.2015.02.024 ↗
30. Li · Encyclopedia of Microfluidics and Nanofluidics · 2008 · 793
31. An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition
32. A computer vision based vehicle detection and counting system
33. Computer vision classifier and platform for automatic counting: More than cars
34. Automatic live fingerlings counting using computer vision