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A Vehicle Detection Method Based on an Improved U-YOLO Network for High-Resolution Remote-Sensing Images

doi: 10.3390/su151310397
The lack of vehicle feature information and the limited number of pixels in high-definition remote-sensing images causes difficulties in vehicle detection. This paper proposes U-YOLO, a vehicle detection method that integrates multi-scale features, attention mechanisms, and sub-pixel convolution. The adaptive fusion module (AF) is added to the backbone of the YOLO detection model to increase the underlying structural information of the feature map. Cross-scale channel attention (CSCA) is introduced to the feature fusion part to obtain the vehicle’s explicit semantic information and further refine the feature map. The sub-pixel convolution module (SC) is used to replace the linear interpolation up-sampling of the original model, and the vehicle target feature map is enlarged to further improve the vehicle detection accuracy. The detection accuracies on the open-source datasets NWPU VHR-10 and DOTA were 91.35% and 71.38%. Compared with the original network model, the detection accuracy on these two datasets was increased by 6.89% and 4.94%, respectively. Compared with the classic target detection networks commonly used in RFBnet, M2det, and SSD300, the average accuracy rate values increased by 6.84%, 6.38%, and 12.41%, respectively. The proposed method effectively solves the problem of low vehicle detection accuracy. It provides an effective basis for promoting the application of high-definition remote-sensing images in traffic target detection and traffic flow parameter detection.
- Minjiang University China (People's Republic of)
- Minjiang University China (People's Republic of)
- Wuhan University of Technology China (People's Republic of)
- Wuhan Polytechnic University China (People's Republic of)
- Xinjiang University China (People's Republic of)
Environmental effects of industries and plants, TJ807-830, TD194-195, remote-sensing images, Renewable energy sources, vehicle inspection, Environmental sciences, cross-scale channel attention, GE1-350, U-YOLO
Environmental effects of industries and plants, TJ807-830, TD194-195, remote-sensing images, Renewable energy sources, vehicle inspection, Environmental sciences, cross-scale channel attention, GE1-350, U-YOLO
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).10 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
