# VSSA-NET: Vertical Spatial Sequence Attention Network for Traffic Sign Detection

arXiv: https://arxiv.org/abs/1905.01583 · HTML: https://mlsanity.com/paper/1905.01583/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2019.

## Built on by (most cited first, 6 of 6)

- [Attention, please! A survey of Neural Attention Models in Deep Learning](https://mlsanity.com/paper/2103.16775/) (2021)
- [RRNet: Relational Reasoning Network with Parallel Multi-scale Attention for Salient Object Detection in Optical Remote Sensing Images](https://mlsanity.com/paper/2110.14223/) (2021)
- [ASK: Adaptively Selecting Key Local Features for RGB-D Scene Recognition](https://mlsanity.com/paper/2110.07703/) (2021)
- [Pyramid Transformer for Traffic Sign Detection](https://mlsanity.com/paper/2207.06067/) (2022)
- [BiP-Net: Bidirectional Perspective Strategy based Arbitrary-Shaped Text Detection Network](https://mlsanity.com/paper/2104.04903/) (2021)
- [A Robust Deep Networks based Multi-Object MultiCamera Tracking System for City Scale Traffic](https://mlsanity.com/paper/2505.00534/) (2025)

## Abstract (arXiv)

Although traffic sign detection has been studied for years and great progress has been made with the rise of deep learning technique, there are still many problems remaining to be addressed. For complicated real-world traffic scenes, there are two main challenges. Firstly, traffic signs are usually small size objects, which makes it more difficult to detect than large ones; Secondly, it is hard to distinguish false targets which resemble real traffic signs in complex street scenes without context information. To handle these problems, we propose a novel end-to-end deep learning method for traffic sign detection in complex environments. Our contributions are as follows: 1) We propose a multi-resolution feature fusion network architecture which exploits densely connected deconvolution layers with skip connections, and can learn more effective features for the small size object; 2) We frame the traffic sign detection as a spatial sequence classification and regression task, and propose a vertical spatial sequence attention (VSSA) module to gain more context information for better detection performance. To comprehensively evaluate the proposed method, we do experiments on several traffic sign datasets as well as the general object detection dataset and the results have shown the effectiveness of our proposed method.