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- W3126804317 abstract "The object detection based on deep learning is an important application in the field of vehicle environment perception, which has been a hot topic in recent years. We propose a novel improved Yolov3-tiny to implement more accurate object detection for the objects in traffic scenes. We employ K-means algorithm to cluster the common objects in traffic scenes to obtain a suitable size and numbers of anchor box. In addition, we modify modifying detection scale and the backbone network structure of Yolov3-tiny, improving the detection accuracy for small object. The stereo vision is also introduced to improve the accuracy of boundary location. Experiments results demonstrate that the improved yolo-tiny has higher accuracy than the original algorithm and it also meet the requirement of real-time performance." @default.
- W3126804317 created "2021-02-15" @default.
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- W3126804317 date "2020-12-18" @default.
- W3126804317 modified "2023-09-27" @default.
- W3126804317 title "Object detection algorithm based on improved Yolov3-tiny network in traffic scenes" @default.
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- W3126804317 doi "https://doi.org/10.1109/cvci51460.2020.9338478" @default.
- W3126804317 hasPublicationYear "2020" @default.
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