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Traffic sign detection has achieved promising results in recent years. Nevertheless, there are still two problems remain to be overcome. One problem is the detection of small traffic signs, which usually occupy less than 2% of the image area. The other problem is fine-grained classification, with difficulties arising from similar appearances between traffic signs. For example, different speed-limit traffic signs have differences solely from the speed numbers. In this paper, we propose a Feature Aggregation MultiPath Network (FAMN) to tackle the problems simultaneously. First, we propose a Feature Aggregation (FA) structure to aggregate regional features from different feature maps by using element-wise Max, then convolution layers are used to extract rich semantic features. Accordingly, objects of different scales can choose the best features to improve performance of small object detection. Second, we propose a Multipath Network (MN) structure to obtain fine-grained features. The MN structure consists of three paths, extracting instance-level, part-level, and context-level features, respectively. The three types of features are then concatenated to form fine-grained features of the proposals. Experimental results demonstrate the effectiveness of our proposed FAMN. Specifically, FAMN is able to obtain an average F1-measure of 93.1% in TT100K dataset, 2.9% higher than the state-of-the-art.

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