@article{ref1,
title="Dense-refinedet for traffic sign detection and classification",
journal="Sensors (Basel)",
year="2020",
author="Sun, Chang and Ai, Yibo and Wang, Sheng and Zhang, Weidong",
volume="20",
number="22",
pages="e6570-e6570",
abstract="Detecting and classifying real-life small traffic signs from large input images is difficult due to their occupying fewer pixels relative to larger targets. To address this challenge, we proposed a deep-learning-based model (Dense-RefineDet) that applies a single-shot, object-detection framework (RefineDet) to maintain a suitable accuracy-speed trade-off. We constructed a dense connection-related transfer-connection block to combine high-level feature layers with low-level feature layers to optimize the use of the higher layers to obtain additional contextual information. Additionally, we presented an anchor-design method to provide suitable anchors for detecting small traffic signs. Experiments using the Tsinghua-Tencent 100K dataset demonstrated that Dense-RefineDet achieved competitive accuracy at high-speed detection (0.13 s/frame) of small-, medium-, and large-scale traffic signs (recall: 84.3%, 95.2%, and 92.6%; precision: 83.9%, 95.6%, and 94.0%). Moreover, experiments using the Caltech pedestrian dataset indicated that the miss rate of Dense-RefineDet was 54.03% (pedestrian height > 20 pixels), which outperformed other state-of-the-art methods.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s20226570",
url="http://dx.doi.org/10.3390/s20226570"
}