Remote Sensing Studies Applied to the Use of Satellite Images in Global Scale

Silva, Luis; Oliveira, Marcos L.S.

doi:10.3390/su15043459

Cited by 1 publication

(1 citation statement)

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“…The rapid development of intelligent transportation in recent years, coupled with the increasing ability of remote-sensing technology to acquire Earth observation data, has led to more attention being paid to vehicle detection using high-resolution remotesensing images. As the foundation and core of smart travel, vehicle detection has important practical significance for target tracking and event detection [1][2][3][4][5]. However, traditional vehicle detection methods have a high installation cost and a large installation workload.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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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.

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