Abstract
The key to reaching full autonomous vehicles lies in the degree of robustness and generalization of the visual perception system for any prompt scenario during driving or under any weather condition. Regardless of all the advent in semantic segmentation and the numerous datasets existing, they still fail to perform well when the input images are unclear (fog, rain, snow, or nighttime), or small-scale. In this study, we will highlight the impact of choosing the adequate coarse ground truth annotation on achieving the best outcome possible out of the preparation and training of the dataset to increase the accuracy of the perception system in driverless cars. Our experiment has shown that indeed the accuracy values of semantic image segmentation using coarse ground truth annotation outperform the fine ground truth for the principal classes (car, road) which implies faster datasets preparation and tuning before pragmatic applications.
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Acknowledgment
W. Jebrane acknowledges support from the “Center National for Scientific and Technical Research” CNRST, Morocco.
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Bouasria, I., Jebrane, W., Akchioui, N.E. (2023). The Role of Ground Truth Annotation in Semantic Image Segmentation Performance for Autonomous Driving. In: Lazaar, M., En-Naimi, E.M., Zouhair, A., Al Achhab, M., Mahboub, O. (eds) Proceedings of the 6th International Conference on Big Data and Internet of Things. BDIoT 2022. Lecture Notes in Networks and Systems, vol 625. Springer, Cham. https://doi.org/10.1007/978-3-031-28387-1_23
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