Web-Based Traffic-Sign Detection
Kasper Fromm Pedersen, Kristian Torp · 2023
Detecting traffic signs on images has many applications within the transportation area, e.g., speed limit detection, navigation, asset management, and autonomous vehicles. A significant challenge in detecting traffic signs is that a large set of labeled images are needed to train, test, and validate an object detector. In this paper, we demonstrate a webapp that enables traffic-sign detection of 169 common traffic signs and downloads the results. Users can contribute to improving object detection by (1) annotating user-uploaded images, (2) verifying traffic signs detected on existing imagery, or (3) donating new imagery by uploading images, e.g., taken with smartphones. The set of detected traffic signs is stepwise increased by correcting mistakes in object detection using the online verification part of the system. The user can immediately download all verified traffic-sign objects. Users can also download 43,995 traffic-sign objects from 378 different classes. This existing set of traffic-sign objects is updated nightly with objects two or more users have verified. The demonstration includes detecting traffic signs on imagery uploaded by the audience and downloading the traffic sign detected, e.g., to be used internally in an organization.