Simultaneous Traffic Sign Detection and Boundary Estimation using Convolutional Neural Network

Abstract

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.

Accepted for publication in IEEE Transactions on Intelligent Transportation Systems

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• Very Deep Convolutional Networks for Large-Scale Image Recognition
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Cited by in corpus (5)

• Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges
• Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems
• Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Review
• Did You Miss the Sign? A False Negative Alarm System for Traffic Sign Detectors
• DFR-TSD: A Deep Learning Based Framework for Robust Traffic Sign Detection Under Challenging Weather Conditions