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A platform for research: civil engineering, architecture and urbanism
Real-Time Vehicle Detection Based on Improved YOLO v5
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.
Real-Time Vehicle Detection Based on Improved YOLO v5
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.
Real-Time Vehicle Detection Based on Improved YOLO v5
Yu Zhang (author) / Zhongyin Guo (author) / Jianqing Wu (author) / Yuan Tian (author) / Haotian Tang (author) / Xinming Guo (author)
2022
Article (Journal)
Electronic Resource
Unknown
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