Multiclass objects detection algorithm using DarkNet-53 and DenseNet for intelligent vehicles Article Swipe
Lina Yang
,
Gang Chen
,
Wenyan Ci
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1186/s13634-023-01045-8
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1186/s13634-023-01045-8
Intelligent vehicles should not only be able to detect various obstacles, but also identify their categories so as to take an appropriate protection and intervention. However, the scenarios of object detection are usually complex and changeable, so how to balance the relationship between accuracy and speed is a difficult task of object detection. This paper proposes a multi-object detection algorithm using DarkNet-53 and dense convolution network (DenseNet) to further ensure maximum information flow between layers. Three 8-layer dense blocks are used to replace the last three downsampling layers in DarkNet-53 structure, so that the network can make full use of multi-layer convolution features before prediction. The loss function of coordinate prediction error in YOLOv3 is further improved to improve the detection accuracy. Extensive experiments are conducted on the public KITTI and Pascal VOC datasets, and the results demonstrate that the proposed algorithm has better robustness, and the network model is more suitable for the traffic scene in the real driving environment and has better adaptability to the objects with long distance, small size and partial occlusion.
Related Topics
Concepts
Computer science
Robustness (evolution)
Pascal (unit)
Object detection
Artificial intelligence
Adaptability
Convolution (computer science)
Algorithm
Convolutional neural network
Upsampling
Computer vision
Data mining
Artificial neural network
Pattern recognition (psychology)
Image (mathematics)
Biology
Chemistry
Biochemistry
Programming language
Ecology
Gene
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1186/s13634-023-01045-8
- https://asp-eurasipjournals.springeropen.com/counter/pdf/10.1186/s13634-023-01045-8
- OA Status
- gold
- Cited By
- 24
- References
- 40
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385461167
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4385461167Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1186/s13634-023-01045-8Digital Object Identifier
- Title
-
Multiclass objects detection algorithm using DarkNet-53 and DenseNet for intelligent vehiclesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-08-01Full publication date if available
- Authors
-
Lina Yang, Gang Chen, Wenyan CiList of authors in order
- Landing page
-
https://doi.org/10.1186/s13634-023-01045-8Publisher landing page
- PDF URL
-
https://asp-eurasipjournals.springeropen.com/counter/pdf/10.1186/s13634-023-01045-8Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://asp-eurasipjournals.springeropen.com/counter/pdf/10.1186/s13634-023-01045-8Direct OA link when available
- Concepts
-
Computer science, Robustness (evolution), Pascal (unit), Object detection, Artificial intelligence, Adaptability, Convolution (computer science), Algorithm, Convolutional neural network, Upsampling, Computer vision, Data mining, Artificial neural network, Pattern recognition (psychology), Image (mathematics), Biology, Chemistry, Biochemistry, Programming language, Ecology, GeneTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
24Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 8, 2024: 12, 2023: 4Per-year citation counts (last 5 years)
- References (count)
-
40Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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