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Using Echo Ultrasound from Schooling Fish to Detect and Classify Fish Types
Handoko Y.a, Nazaruddin Yul.Y.a, Hu H.b
a Department of Engineering Physics, Bandung Institute of Technology, Indonesia
b School of Computer Science and Electronic Engineering, University of Essex, United Kingdom
[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]Fish finders have already been widely available in the fishing market for a number of years. However, the sizes of these fish finders are too big and their prices are expensive to suit for the research of robotic fish or mini-submarine. The goal of this research is to propose a low-cost fish detector and classifier which suits for underwater robot or submarine as a proximity sensor. With some pre-condition in hardware and algorithms, the experimental results show that the proposed design has good performance, with a detection rate of 100% and a classification rate of 94%. Both the existing type of fish and the group behavior can be revealed by statistical interpretations such as hovering passion and sparse swimming mode. © 2009 Jilin University.[/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]artificial neural network,classification,Classification rates,Detection rates,Fish finders,Group behavior,Robotic fishes,Statistical interpretation,Swimming mode,ultrasound sensor,Underwater robots[/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]artificial neural network,classification,fish detection,ultrasound sensor[/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]This research was fund by Fundamental Research Fund Program from Indonesia Ministry of Research and Technology in years 2007. ID Number: RD-2009-2550.[/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/10.1016/S1672-6529(08)60120-1[/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]