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• W4283722204 abstract "For accurate and effective automatic vehicle identification, morphological detection and deep convolutional networks were combined to propose a method for locating and identifying vehicle models from unmanned aerial vehicle (UAV) videos. First, the region of interest of the video frame image was sketched and grey-scale processing was performed; sub-pixel-level skeleton images were generated based on the Canny edge detection results of the region of interest; then, the image skeletons were decomposed and reconstructed. Second, a combination of morphological operations and connected domain morphological features were applied for vehicle target recognition, and a deep learning image benchmark library containing 244,520 UAV video vehicle samples was constructed. Third, we improved the AlexNet model by adding convolutional layers, pooling layers, and adjusting network parameters, which we named AlexNet*. Finally, a vehicle recognition method was established based on a candidate target extraction algorithm with AlexNet*. The validation analysis revealed that AlexNet* achieved a mean F1 of 85.51% for image classification, outperforming AlexNet (82.54%), LeNet (63.88%), CaffeNet (46.64%), VGG16 (16.67%), and GoogLeNet (14.38%). The mean values of Pcor, Pre, and Pmiss for cars and buses reached 94.63%, 6.87%, and 4.40%, respectively, proving that this method can effectively identify UAV video targets." @default.
• W4283722204 created "2022-07-01" @default.
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• W4283722204 date "2022-06-29" @default.
• W4283722204 modified "2023-10-15" @default.
• W4283722204 title "Vehicle Recognition from Unmanned Aerial Vehicle Videos Based on Fusion of Target Pre-Detection and Deep Learning" @default.
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• W4283722204 doi "https://doi.org/10.3390/su14137912" @default.
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