Year-end Sale % OFF 
Abstract
For safe autonomous driving, deep neural network (DNN)-based perception systems play essential roles, where a vast amount of driving images should be manually collected and labeled with ground truth (GT) for training and validation purposes. After observing the manual GT generation’s high cost and unavoidable human errors, this study presents an open-source automatic GT generation tool, CarFree, based on the Carla autonomous driving simulator. By that, we aim to democratize the daunting task of (in particular) object detection dataset generation, which was only possible by big companies or institutes due to its high cost. CarFree comprises (i) a data extraction client that automatically collects relevant information from the Carla simulator’s server and (ii) a post-processing software that produces precise 2D bounding boxes of vehicles and pedestrians on the gathered driving images. Our evaluation results show that CarFree can generate a considerable amount of realistic driving images along with their GTs in a reasonable time. Moreover, using the synthesized training images with artificially made unusual weather and lighting conditions, which are difficult to obtain in real-world driving scenarios, CarFree significantly improves the object detection accuracy in the real world, particularly in the case of harsh environments. With CarFree, we expect its users to generate a variety of object detection datasets in hassle-free ways.
Highlights
For autonomous driving, developing camera-based perception systems requires a tremendous amount of training images, usually collected and labeled by human labor that is costly and error-prone
In recent times, synthesized datasets from 3D game engines are gaining wide acceptance as a viable solution [1,2,3,4]. This new approach has been advocated with compelling results when training deep neural networks (DNNs) for perception systems [5,6]
As an initial effort towards democratizing the daunting task of dataset generation for perception systems, this study presents an open-source image synthesis and automatic ground truth (GT) generation software, for camera-based vehicle and pedestrian detection
Summary
For autonomous driving, developing camera-based perception systems requires a tremendous amount of training images, usually collected and labeled by human labor that is costly and error-prone. In recent times, synthesized datasets from 3D game engines are gaining wide acceptance as a viable solution [1,2,3,4]. This new approach has been advocated with compelling results when training deep neural networks (DNNs) for perception systems [5,6]. Public datasets are inherently limited in quantity and diversity compared with proprietary datasets owned by big companies. Gathering more data and corner cases is becoming more critical than inventing new DNN architectures these days
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
