# Simultaneous Traffic Sign Detection and Boundary Estimation using Convolutional Neural Network

arXiv: https://arxiv.org/abs/1802.10019 · HTML: https://mlsanity.com/paper/1802.10019/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2018.

## Built on by (most cited first, 4 of 4)

- [Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2304.10410/) (2023)
- [RDD4D: 4D Attention-Guided Road Damage Detection And Classification](https://mlsanity.com/paper/2501.02822/) (2025)
- [Road Traffic Sign Recognition method using Siamese network Combining Efficient-CNN based Encoder](https://mlsanity.com/paper/2502.15307/) (2025)
- [Contrastive Learning-Driven Traffic Sign Perception: Multi-Modal Fusion of Text and Vision](https://mlsanity.com/paper/2507.23331/) (2025)

## Abstract (arXiv)

We propose a novel traffic sign detection system that simultaneously estimates the location and precise boundary of traffic signs using convolutional neural network (CNN). Estimating the precise boundary of traffic signs is important in navigation systems for intelligent vehicles where traffic signs can be used as 3D landmarks for road environment. Previous traffic sign detection systems, including recent methods based on CNN, only provide bounding boxes of traffic signs as output, and thus requires additional processes such as contour estimation or image segmentation to obtain the precise sign boundary. In this work, the boundary estimation of traffic signs is formulated as a 2D pose and shape class prediction problem, and this is effectively solved by a single CNN. With the predicted 2D pose and the shape class of a target traffic sign in an input image, we estimate the actual boundary of the target sign by projecting the boundary of a corresponding template sign image into the input image plane. By formulating the boundary estimation problem as a CNN-based pose and shape prediction task, our method is end-to-end trainable, and more robust to occlusion and small targets than other boundary estimation methods that rely on contour estimation or image segmentation. The proposed method with architectural optimization provides an accurate traffic sign boundary estimation which is also efficient in compute, showing a detection frame rate higher than 7 frames per second on low-power mobile platforms.