Journal
SUSTAINABILITY
Volume 14, Issue 19, Pages -Publisher
MDPI
DOI: 10.3390/su141912274
Keywords
object detection; YOLO v5; Flip-Mosaic algorithm; image processing
Funding
- Science and Technology Project of Shandong Provincial Department of Transportation [2019B32]
- Key Science and Technology Projects of the Ministry of Transport of the People's Republic of China [2019-ZD7-051]
- National Nature Science Foundation of China [52002224]
- major scientific and technological innovation project of Shandong Province [2020CXGC010118]
- National Nature Science Foundation of Jiangsu Province [BK20200226]
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This paper proposes an improved method for vehicle detection in different traffic scenarios based on an improved YOLO v5 network to reduce the false detection rate caused by occlusion. The proposed method enhances the network's perception of small targets through the Flip-Mosaic algorithm. A multi-type vehicle target dataset collected in different scenarios is used for training the detection model. Experimental results demonstrate that the Flip-Mosaic data enhancement algorithm can improve the accuracy of vehicle detection and reduce the false detection rate.
To reduce the false detection rate of vehicle targets caused by occlusion, an improved method of vehicle detection in different traffic scenarios based on an improved YOLO v5 network is proposed. The proposed method uses the Flip-Mosaic algorithm to enhance the network's perception of small targets. A multi-type vehicle target dataset collected in different scenarios was set up. The detection model was trained based on the dataset. The experimental results showed that the Flip-Mosaic data enhancement algorithm can improve the accuracy of vehicle detection and reduce the false detection rate.
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