Object Sorting using Faster R-CNN
Open accessAbstract
In a factory production line, different industry parts need to be quickly differentiated and sorted for further process. Parts can be of different colors and shapes. It is tedious for humans to differentiate and sort these objects in appropriate categories. Automating this process would save more time and cost. In the automation process, choosing an appropriate model to detect and classify different objects based on specific features is more challenging. In this paper, three different neural network models are compared to the object sorting system. They are namely CNN, Fast R-CNN, and Faster R-CNN. These models are tested, and their performance is analyzed. Moreover, for the object sorting system, an Arduino-controlled 5 DoF (degree of freedom) robot arm is programmed to grab and drop symmetrical objects to the targeted zone. Objects are categorized into classes based on color, defective and non-defective objects.
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A faster R-CNN model using a dataset of symmetric objects improved accuracy rate of detecting defective and non-defective objects by increasing the training dataset to up to 400 images.
“The accuracy rate of detecting defective and non-defective objects is successfully improved, increasing the training dataset to up to 400 images of defective and non-defective objects.”
From Advanced Applications of Industrial Robotics: New Trends and Possibilities (2021) · cites this paper as [39]
The Result Analysis section reports that accuracy rose as the training share of the dataset increased, from 78.83% at 70% training to 85.5% at 90% training. It also says the dataset contained 400 images of defective and intact cubes. The 400 images were the total dataset, however, not the number used for training; the largest reported training share was 90%. (LLM, conf=0.88)
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Cites
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 56,035 · see citing works
Flagged first, then by citations
Research works printed but not linked: 11
- Z. Zhao, P. Zheng, S. Xu and X. Wu, "Object Detection With Deep Learning: A Review," in IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 11, pp. 3212-3232, Nov. 2019, doi: 10.1109/TNNLS.2018.2876865. · DOI · Paper page
- L. BinYan, W. YanBo, C. ZhiHong, L. JiaYu and L. JunQin, "Object detection and robotic sorting system in complex industrial environment," 2017 Chinese Automation Congress (CAC), Jinan, 2017, pp. 7277-7281, doi: 10.1109/CAC.2017.8244092. · DOI
- H. Basri, I. Syarif and S. Sukaridhoto, "Faster R-CNN Implementation Method for Multi-Fruit Detection Using Tensorflow Platform," 2018 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC), Bali, Indonesia, 2018, pp. 337-340, doi: 10.1109/KCIC.2018.8628566. · DOI
- T. Wang, Y. Yao, Y. Chen, M. Zhang, F. Tao and H. Snoussi, "Auto-Sorting System Toward Smart Factory Based on Deep Learning for Image Segmentation," in IEEE Sensors Journal, vol. 18, no. 20, pp. 8493-8501, 15 Oct.15, 2018, doi: 10.1109/JSEN.2018.2866943. · DOI
- H. Shin et al., "Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning," in IEEE Transactions on Medical Imaging, vol. 35, no. 5, pp. 1285-1298, May 2016, doi: 10.1109/TMI.2016.2528162. · DOI · Paper page
- R. Tylecek. Probabilistic Models for Symmetric Object Detection in Images. PhD thesis, Czech Technical Univ. Prague, 2016.
- J. Hsieh, L. Chen and D. Chen, "Symmetrical SURF and Its Applications to Vehicle Detection and Vehicle Make and Model Recognition," in IEEE Transactions on Intelligent Transportation Systems, vol. 15, no. 1, pp. 6-20, Feb. 2014, doi: 10.1109/TITS.2013.2294646. · DOI · Paper page
- J. Liu, B. Li, Y. Xiong, B. He, L. Li, "Integrating the Symmetry Image and Improved Sparse Representation for Railway Fastener Classification and Defect Recognition", Hindawi Publishing Corporation Mathematical Problems in Engineering, Vol. 2015, 18 October 2015.
- Priya Vinayak Garad, "Object Sorting Robot Based On the Shape", International Journal Of Advance Research, Ideas And Innovations In Technology, Vol.3, Issue 5, pp. 129-134.
- R. Girshick et al., "Rich feature hierarchies for accurate object detection and semantic segmentation", Proc. CVPR, 2014, pp. 580-587.
- R. Girshick, "Fast R-CNN", Proc. ICCV, 2015, pp.1440-1448.
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Sorted by citations
Advanced Applications of Industrial Robotics: New Trends and Possibilities
Dzedzickis, A., Subačiūtė-Žemaitienė, J., Šutinys, E., Samukaitė-Bubnienė, U., & Bučinskas, V. (2021) · Applied Sciences · doi:10.3390/app12010135 · Cited by 228 · see citing works
- Authors
- Chen, P., & Elangovan, V.
- Affiliations
- Pennsylvania State University (US)
- ORCID
- Elangovan, Vinayak
- Venue
- International Journal of Artificial Intelligence & Applications
- Published
- 2020-11-30
- Volume
- 11
- Issue
- 6
- Pages
- 27-36
- ISSN
- 0975-900X (ISSN-L)
- 0976-2191
- OpenAlex
- W3111141471
- Cited by
- 9
- References
- 1
- Citations by year
- 2026: 1 · 2025: 2 · 2023: 3 · 2022: 1 · 2021: 2
- FWCI
- 0.56
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Journal of Artificial Intelligence & Applications (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Keywords
- object sorting, Faster R-CNN, object detection, industrial automation, robot manipulators, 5-DOF robot arm, color classification, model comparison
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