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- W2949817882 abstract "Object detection based on the deep learning has achieved very good performances. However, there are many problems with images in real-world shooting such as noise, blurring and rotating jitter, etc. These problems have an important impact on object detection. Using traffic signs as an example, we established image degradation models which are based on YOLO network and combined traditional image processing methods to simulate the problems existing in real-world shooting. After establishing the different degradation models, we compared the effects of different degradation models on object detection. We used the YOLO network to train a robust model to improve the average precision (AP) of traffic signs detection in real scenes." @default.
- W2949817882 created "2019-06-27" @default.
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- W2949817882 date "2018-12-01" @default.
- W2949817882 modified "2023-10-16" @default.
- W2949817882 title "Object Detection Based on YOLO Network" @default.
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- W2949817882 doi "https://doi.org/10.1109/itoec.2018.8740604" @default.
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