A novel optimized tiny YOLOv3 algorithm for the identification of objects in the lawn environment

Based on the problem of insufficient accuracy of the original tiny YOLOv3 algorithm for object detection in a lawn environment, an Optimized tiny YOLOv3 algorithm with less computation and higher accuracy is proposed. Three reasons affect the accuracy of the original tiny YOLOv3 algorithm for detect...

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Veröffentlicht in:Scientific reports Jg. 12; H. 1; S. 15124 - 9
Hauptverfasser: Wang, Xinyan, Lv, Feng, Li, Lei, Yi, Zhengyang, Jiang, Quan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 06.09.2022
Nature Publishing Group
Nature Portfolio
Schlagworte:
639/166/988
639/705/117
Accuracy
Algorithms
Humanities and Social Sciences
Humans
Information processing
multidisciplinary
Neural Networks, Computer
Science
Science (multidisciplinary)
ISSN:2045-2322, 2045-2322
Online-Zugang:Volltext
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Zusammenfassung:Based on the problem of insufficient accuracy of the original tiny YOLOv3 algorithm for object detection in a lawn environment, an Optimized tiny YOLOv3 algorithm with less computation and higher accuracy is proposed. Three reasons affect the accuracy of the original tiny YOLOv3 algorithm for detecting objects in a lawn environment. First, the backbone of the original algorithm is composed of a stack of a single convolutional layer and a max-pooling layer, which results in insufficient ability to extract feature information of objects. An enhancement module is proposed to enhance the feature extraction capability of the shallow layers of the network. Second, the information of the shallow convolutional layers of the backbone is not fully used, which results in insufficient detection capability for small objects. Third, the deep part of the backbone uses a convolutional layer with an excessive number of channels, which results in a large amount of computation. A multi-resolution fusion module is proposed to enhance the information interaction capability of the deep and shallow layers of the network, and reduce the computation. To verify the accuracy of this Optimized tiny YOLOv3 algorithm, the algorithm was tested on the dataset containing trunk, spherical tree and person, and compared with the current research. The results show that the algorithm proposed in this paper improves the detection accuracy while reducing the calculation.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-022-19519-4