Vehicle Recognition from Unmanned Aerial Vehicle Videos Based on Fusion of Target Pre-Detection and Deep Learning

Bo Peng, Hanbo Zhang, Ni Yang and Jiming Xie
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Bo Peng: College of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China
Hanbo Zhang: Faculty of Transportation and Engineering, Kunming University of Science and Technology, Kunming 650504, China
Ni Yang: Faculty of Transportation and Engineering, Kunming University of Science and Technology, Kunming 650504, China
Jiming Xie: Faculty of Transportation and Engineering, Kunming University of Science and Technology, Kunming 650504, China

Sustainability, 2022, vol. 14, issue 13, 1-21

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 F 1 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 P c o r , P r e , and P m i s s for cars and buses reached 94.63%, 6.87%, and 4.40%, respectively, proving that this method can effectively identify UAV video targets.

Keywords: intelligent traffic; vehicle recognition; deep learning; drone video; morphological detection (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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