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Vehicle detection in aerial images based on lightweight Deep Convolutional Network and Generative Adversarial Network
Version 2 2020-05-05, 13:27
Version 1 2020-05-05, 13:24
journal contributionposted on 2020-05-05, 13:27 authored by J Shen, NZ Liu, H Sun, Huiyu Zhou
Vehicle detection in aerial images is a challenging task and plays an important role in a wide range of applications. Traditional detection algorithms are based on sliding-window searching and shallowlearning-based features, which limits the ability to represent features and generates a lot of computational costs. Recently, with the successful application of convolutional neural network in computer vision, many state-of-the-art detectors have been developed based on deep CNNs. However, these CNN-based models still face some difficulties and challenges in vehicle detection in aerial images. Firstly, the CNN-based detection model requires extensive calculations during training and detection, and the accuracy of detection for small objects is not high. In addition, deep learning models often require a large amount of sample data to train a robust detection model, while the annotated data of aerial vehicles is limited. In this study, we propose a lightweight deep convolutional neural network detection model named LD-CNNs. The detection algorithm not only greatly reduces the computational costs of the model, but also significantly improves the accuracy of the detection. What’s more, in order to cope with the problem of insufficient training samples, we develop a multi-condition constrained generative adversarial network named MC-GAN, which can effectively generate samples. The detection performance of the proposed model has been evaluated on the Munich public dataset and the collected dataset respectively. The results show that on the Munich dataset, the proposed method achieves 86.9% on mAP (mean average precision), F1-score is 0.875, and the detection time is 1.64s on Nvidia Titan XP. At present, these detection indicators have reached state-of-the-art level in vehicle detection of aerial images.
This work was supported in part by the National Natural Science Foundation of China under Grant 61375021, in part by the Fundamental Research Funds for the Central Universities under Grant NS2016091, and in part by the Collaborative Innovation Center of Novel Software Technology and Industrialization.
CitationIEEE Access, 2019, 7 , pp. 148119 - 148130
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