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A system of semantic segmentation on an autonomous vehicle
Lazuardi R.N.a, Sudrajat D.a, Aulia N.a, Adiono T.a
a Institut Teknologi Bandung, School of Electrical Engineering and Informatics (STEI), Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2019 IEEE.The existing computer vision system on autonomous vehicle now is still depending on an intensive computational system from GPU. Not only power-consuming, but it’s also taking a considerable space on a self-driving car. This paper attempts to design an efficient and low power consumption by utilizing a deep learning system on an embedded system that is raspberry pi 3 B+ on this case. It is more environmentally friendly and also using a much more little space than existing system. To be specific, this paper will implement a semantic segmentation task on an embedded system. The U-Net model for semantic segmentation will be optimized to require less computational system as possible by tuning down the hyperparameter needed. The segmentation task will be using cityscapes dataset and using total to 5 labels, i.e. human, road, vehicles, background, and a neutral class which will be neglected. Using pixel accuracy as our evaluation matrix, our model achieved an accuracy of 96.03% and 92.14% both on training and validation set. The inference time needed to segments an image is 0,5s and processing time on a video test is 1.8 FPS.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Computational system,Computer vision system,Evaluation matrices,Existing systems,Hyper-parameter,Low-power consumption,Power consuming,Semantic segmentation[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Deep learning,Embedded system,Semantic segmentation.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ECTI-CON47248.2019.8955214[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]