https://scholars.lib.ntu.edu.tw/handle/123456789/581325
標題: | IMMVP: An Efficient Daytime and Nighttime On-Road Object Detector | 作者: | Wu C.-E Chan Y.-M Chen C.-H Chen W.-C CHU-SONG CHEN |
關鍵字: | Deep learning; Embedded systems; Feature extraction; Iterative methods; Multimedia signal processing; Roads and streets; Detecting objects; Detection accuracy; Feature extractor; Feature pyramid; Lighting conditions; Object detectors; Pedestrian detection; Vehicle detection; Object detection | 公開日期: | 2019 | 來源出版物: | IEEE 21st International Workshop on Multimedia Signal Processing, MMSP 2019 | 摘要: | It is hard to detect on-road objects under various lighting conditions. To improve the quality of the classifier, three techniques are used. We define subclasses to separate daytime and nighttime samples. Then we skip similar samples in the training set to prevent overfitting. With the help of the outside training samples, the detection accuracy is also improved. To detect objects in an edge device, Nvidia Jetson TX2 platform, we exert the lightweight model ResNet-18 FPN as the backbone feature extractor. The FPN (Feature Pyramid Network) generates good features for detecting objects over various scales. With Cascade R-CNN technique, the bounding boxes are iteratively refined for better results. ? 2019 IEEE. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075716918&doi=10.1109%2fMMSP.2019.8901824&partnerID=40&md5=a49e4af9ff0963aeb008d2b035c55b83 https://scholars.lib.ntu.edu.tw/handle/123456789/581325 |
DOI: | 10.1109/MMSP.2019.8901824 |
顯示於: | 資訊工程學系 |
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