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

## TL;DR

VSSA-NET introduces a novel deep learning framework that enhances traffic sign detection by effectively handling small objects and complex scenes through multi-resolution feature fusion and a vertical spatial sequence attention module.

## Contribution

It presents a new end-to-end detection method combining multi-resolution feature fusion with a vertical spatial sequence attention module for improved traffic sign detection.

## Key findings

- Effective detection of small traffic signs.
- Improved accuracy in complex street scenes.
- Validated on multiple datasets with positive results.

## Abstract

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.