ISSN 0914-4935
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
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A Study of Bottom-sediment Classification System Using Seabed Images

Jun Kitagawa, Koichiro Enomoto, Masashi Toda, Koji Miyoshi, and Yasuhiro Kuwahara

(Received October 8, 2018; Accepted January 16, 2019)

Keywords: scallop, marine resources, seabed image, bottom sediment, convolutional neural network

In this study, we propose a bottom-sediment classification system using seabed images. Seabed videos using a digital video camera (DV camera) were taken for a fishery-resource survey in the scatter scallop fishing grounds in Hokkaido, Japan. Scatter scallop fishing is a method of waiting for naturally growing young shell on the seabed. We acquired about 0.02 km2 of seabed videos in 2015 in Monbetsu. We cannot survey as wide a range using the DV camera as we can using sonar; however, we can obtain high-resolution 75 × 42 cm2 seabed images. We can classify bottom sediment in a narrower range than bottom-sediment classification methods using sonar. Our research aims to classify four types of bottom sediment: sand, ballast, gravel, and shell bank. The bottom sediment affects the growth of the scallops and the survival rate of the young shells. Therefore, understanding the undersea environment is important. In this study, we used a convolutional neural network (CNN) for the bottom-sediment classification from seabed images. Using CNN enables automatic and high-speed classification. This experiment showed an average accuracy of about 95% for three types of bottom sediment, and 76.5% for the fourth (gravel). Moreover, we created a fishing-ground map based on the bottom sediment for visualizing the seabed environment.

Corresponding author: Jun Kitagawa



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