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- W2972006294 endingPage "128868" @default.
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- W2972006294 abstract "Object detection is one of the most important and challenging branches of computer vision, which has been widely applied in peoples life, such as monitoring security, autonomous driving and so on, with the purpose of locating instances of semantic objects of a certain class. With the rapid development of deep learning networks for detection tasks, the performance of object detectors has been greatly improved. In order to understand the main development status of object detection pipeline, thoroughly and deeply, in this survey, we first analyze the methods of existing typical detection models and describe the benchmark datasets. Afterwards and primarily, we provide a comprehensive overview of a variety of object detection methods in a systematic manner, covering the one-stage and two-stage detectors. Moreover, we list the traditional and new applications. Some representative branches of object detection are analyzed as well. Finally, we discuss the architecture of exploiting these object detection methods to build an effective and efficient system and point out a set of development trends to better follow the state-of-the-art algorithms and further research." @default.
- W2972006294 created "2019-09-12" @default.
- W2972006294 creator A5033300023 @default.
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- W2972006294 creator A5077920175 @default.
- W2972006294 creator A5080902896 @default.
- W2972006294 creator A5085025467 @default.
- W2972006294 date "2019-01-01" @default.
- W2972006294 modified "2023-10-11" @default.
- W2972006294 title "A Survey of Deep Learning-Based Object Detection" @default.
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