Improved Traffic Sign Detection Algorithm Based on Faster R-CNN
Improved Traffic Sign Detection Algorithm Based on Faster R-CNN
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DOI：
10.3390/app12188948
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发表时间：
2022-09-01
影响因子：
2.7
通讯作者：
Li, Yicheng
中科院分区：
文献类型：
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作者：
Gao, Xiang;Chen, Long;Li, Yicheng
基于快速区域卷积神经网络(R-CNN)的交通标志检测算法已经应用于各种智能车辆的驾驶场景。然而，目前的检测算法模型存在一定的缺陷，包括受天气和光照的影响，距离交通标志的检测，以及相似交通标志的检测。针对这些问题，本文提出了一种改进的基于快速R-CNN的交通标志检测方法。首先，提出了一种将特征金字塔融合到更快的R-CNN算法中的融合方法。该融合方法能够较高精度地提取目标特征，减少了天气和光照的影响。其次，在主干网络中加入了可变形卷积(DCN)，它可以训练算法以精确地识别交通标志，使相似标志更具区分性，特别是使其更好地处理失真图像。最后，用ROI Align代替ROI池化，避免了池化带来的远处交通标志细节损失，提高了远处交通标志的检测精度。在TT100k数据集和真实智能车辆上的实验结果表明，该算法在检测小目标交通标志和日落、雨天等低强度环境中的检测性能优于原有的快速R-CNN算法和其他四种最新的交通标志检测方法。因此，该方法有助于提高交通标志在极端环境(弱光或下雨天气)下的检测性能。
The traffic sign detection algorithm based on Faster Region-Based Convolutional Neural Network (R-CNN) has been applied to various intelligent-vehicles driving scenarios. However, the model of the current detection algorithm has certain shortcomings, which include the influence of weather and light, the detection of distance traffic signs, and the detection of similar traffic signs. To solve these problems, this paper proposes an improved traffic sign detection method based on Faster R-CNN. First, we propose a fusion method that fuses the feature pyramid into the Faster R-CNN algorithm. This fusion method can extract object features with precision and decrease the influence of weather and light. Second, a deformable convolution (DCN) which can train the algorithm to identify traffic signs with precision and make similar signs more distinguishable, and in particular make it work better with distorted images, is added to the backbone network. Lastly, we apply ROI align to replace the ROI pooling, which can avoid the distant traffic sign detail loss caused by pooling and increase the detection precision of distant traffic signs. The experimental results on both the TT100k dataset and real intelligent vehicle tests demonstrate that the algorithm is superior to the original Faster R-CNN algorithm and four other state-of-the-art methods in traffic sign detection, specifically in small-target traffic sign detection and low-intensity environments such as sunset time and rainy days. Therefore, the proposed method is helpful to improve the traffic sign detection performance in extreme environments (low-light intensity or rainy weather).