Dietz Romero, Pau
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Publication Synthesising large, low cost and diverse datasets for robust semantic segmentation in self-driving tasksRobust scene understanding algorithms are crucial for the success of autonomous navigation. The supervised learning of semantic segmentation unfortunately requires large and diverse datasets. For some self-driving tasks, such as navigating a robot within an industrial facility, freely available datasets are not available, and manual annotation of large datasets is impractical for smaller development teams. While there are approaches to automatically generate synthetic data, they can be computationally expensive, require significant preparation effort, or miss a wide variety of features. This paper presents a new framework for generating synthetic datasets with high variance for low computing demands that can be easily adapted to different self-driving tasks. The details of the framework can be found at https://github.com/cITIcar/SAD-Generator. As a demonstration, this approach was applied to a semantic segmentation task on a miniature road with random obstacles, lane markings, and disturbing artifacts. A U-Net was trained using synthesized data and later fine-tuned with a small amount of manually annotated data. This resulted in an improvement of 2.5 percentage points in pixel accuracy (PA) and 11.19 percentage points in mean intersection over union (mIoU).Source Type:Other2 12 - Some of the metrics are blocked by yourconsent settings
Publication Access status: Open Access , Synthesising large, low cost and diverse datasets for robust semantic segmentation in self-driving tasksRobust scene understanding algorithms are essential for the success of autonomous navigation. Unfortunately the supervised learning of semantic segmentation requires large and diverse datasets. For certain self-driving tasks like navigating a robot inside an industrial facility no datasets are freely available and the manual annotation of large datasets is impracticable for smaller development teams. Although approaches exist to automatically generate synthetic data, they are either too computational expensive, demand a huge preparation effort or miss a large variety of features. This paper presents a novel framework to generate synthetic datasets with great variance for low computing demand that are easily adaptable to different self-driving tasks (Available at https://github.com/cITIcar/SAD-Generator). As demonstration this approach was applied to a semantic segmentation task on a miniature road with random obstacles, lane markings and disturbing artefacts. Training thus synthesized data in a U-Net and and later fine-tuning it with a small amount of manually annotated data, improved pixel accuracy (PA) by 2.5 percentage points and mean intersection over union (mIoU) by 11.19 percentage points.Source Type:Other4 10
