The DOI matches its Crossref record.
A novel detection fusion network for solid waste sorting
Open accessAbstract
Vision-based object detection technology plays a very important role in the field of computer vision. It is widely used in many machine vision applications. However, in the specific application scenarios, like a solid waste sorting system, it is very difficult to obtain good accuracy due to the color information of objects that is badly damaged. In this work, we propose a novel multimodal convolutional neural network method for RGB-D solid waste object detection. The depth information is introduced as the new modal to improve the object detection performance. Our method fuses two individual features in multiple scales, which forms an end-to-end network. We evaluate our method on the self-constructed solid waste data set. In comparison with single modal detection and other popular cross modal fusion neural networks, our method achieves remarkable results with high validity, reliability, and real-time detection speed.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
An RGB-D-based method for solid waste object detection achieved a mean average precision value of 49.1%.
“Experiments performed on solid waste image analysis resulted in a mean average precision value of 49.1%.”
From Advanced Applications of Industrial Robotics: New Trends and Possibilities (2021) · cites this paper as [40]
A novel multimodal convolutional neural network for RGB-D object detection achieved 49.1% mean average precision, processing images in real-time at 35.3 FPS on one single Nvidia GTX1080 GPU.
“Achieved 49.1% mean average precision, processing images in real-time at 35.3 FPS on one single Nvidia GTX1080 GPU.”
From Advanced Applications of Industrial Robotics: New Trends and Possibilities (2021) · cites this paper as [40]
Citations
Cites
No references could be resolved yet.
Research works printed but not linked: 28
- 10.1109/TIE.2019.2892681 · DOI
- Li B, Zhang T, Xia T. Vehicle detection from 3D lidar using fully convolutional network. arXiv preprint arXiv:160807916 2016.
- Ren S, 2015, Adv Neur Inf Proc Syst, 39, 91
- Soviany P, 2018 20th international symposium on symbolic and numeric algorithms for scientific computing (SYNASC), 209
- Redmon J, Farhadi A. YOLOV3: an incremental improvement. arXiv preprint arXiv:180402767 2018.
- Liu W, European conference on computer vision, 21
- Aytar Y, 2011 International conference on computer vision, 2252
- 10.1109/TII.2013.2248741 · DOI
- Eitel A, 2015 IEEE/RSJ international conference on intelligent robots and systems (IROS), 681
- Gupta S, Proceedings of the IEEE conference on computer vision and pattern recognition, 2827
- Gupta S, European conference on computer vision, 345
- Ngiam J, Proceedings of the 28th international conference on machine learning
- Chen K, Proceedings of the IEEE conference on computer vision and pattern recognition, 5119
- Hinton G, Vinyals O, Dean J. Distilling the knowledge in a neural network. arXiv preprint arXiv:150302531 2015.
- Gupta S, Proceedings of the IEEE conference on computer vision and pattern recognition, 4731
- 10.1016/j.compeleceng.2019.106449 · DOI
- Duan L, Xu D, Tsang I. Learning with augmented features for heterogeneous domain adaptation. arXiv preprint arXiv:12064660 2012.
- Gong B, 2012 IEEE conference on computer vision and pattern recognition, 2066
- Kulis B, CVPR 2011, 1785
- Hinton GE, Srivastava N, Krizhevsky A, et al. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv 12070580 2012.
- 10.1177/1729881417752820 · DOI
- 10.1109/TII.2019.2933999 · DOI
- 10.1109/TPAMI.2017.2706685 · DOI
- 10.1109/TIP.2018.2878956 · DOI
- Yin K, 2018 24th International conference on pattern recognition (ICPR), 2386
- Krizhevsky A, 2012, Adv Neur Inf Proc Syst, 25, 1097
- 10.1109/TVCG.2019.2932216 · DOI
- 10.1109/TIE.2018.2880727 · DOI
Cited by
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
Metadata
- Authors
- Yu, Y., Zou, S., & Yin, K.
- Affiliations
- Zhejiang University of Technology, Hangzhou, Zhejiang, China
- Zhejiang University of Technology (CN)
- Venue
- International Journal of Advanced Robotic Systems
- Publisher
- SAGE Publications
- Published
- 2020-09-01
- Volume
- 17
- Issue
- 5
- ISSN
- 1729-8806 (ISSN-L)
- 1729-8814
- OpenAlex
- W3086639859
- Cited by
- 11
- References
- 0
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 3 · 2023: 1 · 2022: 3 · 2021: 2
- FWCI
- 0.58
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- International Journal of Advanced Robotic Systems (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Department of Education of Zhejiang Province (Y201738129)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Environmental Science › Waste Management and Disposal › Municipal Solid Waste Management
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Keywords
- solid waste sorting, multimodal fusion, depth information, feature fusion, cross-modal fusion, real-time detection
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
