The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Analysis of Object Detection Performance Based on Faster R-CNN
Open accessAbstract
The related regions with convolutional neural networks (R-CNN) models have been widely used in the field of object detection. Faster R-CNN significantly improves the overall performance by adding RPN, especially in terms of detection speed. However, the application of different pre-training models will result in a great difference in the performance of Faster R-CNN. This paper analyzed the performance of Faster R-CNN models based on different pre-training models and conducted a comprehensive evaluation of the performance of Faster R-CNN. The experimental results showed the accuracy and detection speed of R-CNN, fast R-CNN and faster R-CNN based on three different data sets. They can objectively and comprehensively evaluate the performance of R-CNN, fast R-CNN, and faster R-CNN.
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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 [57]
Section 3.3 (Experimental Results; page not provided) reports that Faster R-CNN had higher detection accuracy than R-CNN and Fast R-CNN on three datasets. It does not report detecting defective versus non-defective symmetric objects or improving accuracy by increasing a training set to 400 images. (LLM, conf=0.86)
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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 73,472
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Research works printed but not linked: 4
- He, 2015, Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, 37, 1904, 10.1109/TPAMI.2015.2389824 · DOI · Paper page
- Pathak, 2018, Application of Deep Learning for Object Detection, Procedia Computer Science, 132, 1706, 10.1016/j.procs.2018.05.144 · DOI
- Guo, 2016, Deep learning for visual understanding: A review, Neurocomputing, 187, 27, 10.1016/j.neucom.2015.09.116 · DOI · Paper page
- Sultana, 2020, A Review of Object Detection Models Based on Convolutional Neural Network, 1
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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
Metadata
- Authors
- Li, W.
- Affiliations
- University of Technology Sydney (AU)
- Corresponding author
- Li, Wenze
- ORCID
- Li, Wenze
- Venue
- Journal of Physics: Conference Series
- Publisher
- IOP Publishing
- Published
- 2021-03-01
- Volume
- 1827
- Issue
- 1
- Pages
- 012085
- ISSN
- 1742-6588 (ISSN-L)
- 1742-6596
- OpenAlex
- W3136869208
- Cited by
- 85
- References
- 1
- Citations by year
- 2026: 14 · 2025: 33 · 2024: 16 · 2023: 14 · 2022: 5 · 2021: 3
- FWCI
- 3.05
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Journal of Physics Conference Series (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- Faster R-CNN, object detection, region proposal network, pre-trained models, detection speed, detection accuracy, Fast R-CNN, R-CNN, performance evaluation
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