Abstract
Traffic sign detection is one of the critical technologies in the field of intelligent transportation systems (ITS). The difficulty of traffic sign detection mainly lies in detecting small objects in a wide and complex traffic scene quickly and accurately. In this paper, we regard traffic sign detection as a region classification problem and propose a two-stage CNN-based approach to solve it. At the first stage, we design an efficient network which is built with improved fire-modules to generate object proposals quickly. The network up-samples and merges the feature maps of different scales to attain a high-resolution fused feature map which contains semantically strong features of multi-scale objects. Specially, the prediction is made on the fuse feature map and based on the novel center-point estimation. With the overall designs, our region proposal network can achieve high recall value while using low-resolution images. At the second stage, a separate classification network is proposed. The bottleneck of the classification performance is generally caused by the greatly similar appearances between traffic signs. Therefore, we further explore local regions with critical differences between traffic signs to obtain fine-grained local features which help to improve classification. Finally, we evaluate our method on a challenge benchmark Tsinghua-Tencent 100K which provides many large images with small traffic sign instances. The experiment result shows that our method has better performance and faster detection speed than many state-of-the-art traffic sign detection methods.
Highlights
Traffic sign detection plays an important role in intelligent transportation systems (ITS)
We evaluate the performance of traffic sign detection methods with the regular detection metrics, precision and recall which are the same as those used in the previous study [14]
We find that Faster-RCNN [8] cannot obtain satisfactory results in small traffic sign detection
Summary
Traffic sign detection plays an important role in ITS. An accurate and efficient traffic sign detection detector is able to help human drivers or autonomous driving systems to keep track of road conditions and gain more time to make correct driving operations, which can effectively improve the comfort and safety of driving. L. Wei et al.: Traffic Sign Detection and Recognition Using Novel Center-Point Estimation and Local Features and use machine learning methods such as random forest and support vector machine for classification. MR-CNN [16] uses the multi-scale features and the surrounding context information of the candidate objects to detect traffic signs, but it ignores the important local features. We propose a two-stage CNN-based method to detect small traffic signs in high-resolution images, and achieves a good balance between accuracy and efficiency. It fuses feature maps to attain multi-scale features and uses novel center-point estimation to generate object proposals. The latter further explores critical local regions to extract fine-grained features to improve classification accuracy.
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