Abstract:The quality of semantic image segmentation models can be affected by external factors such as weather or daytime. Those factors can lead to safety-critical mistakes. In this work, we propose a systematic approach to detect and alleviate such weaknesses of semantic segmentation models. We systematically evaluate a semantic segmentation model under different external factors and analyze which factors have the largest impact on the performance. Then, we collect new training data under the most harmful external fa… Show more
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