TY - GEN
T1 - Deep learning on underwater marine object detection
T2 - 18th International Conference on Advanced Concepts for Intelligent Vision Systems
AU - Moniruzzaman, Md
AU - Islam, Syed Mohammed Shamsul
AU - Bennamoun, Mohammed
AU - Lavery, Paul
PY - 2017
Y1 - 2017
N2 - Deep learning, also known as deep machine learning or deep structured learning based techniques, have recently achieved tremendous success in digital image processing for object detection and classification. As a result, they are rapidly gaining popularity and attention from the computer vision research community. There has been a massive increase in the collection of digital imagery for the monitoring of underwater ecosystems, including seagrass meadows. This growth in image data has driven the need for automatic detection and classification using deep neural network based classifiers. This paper systematically describes the use of deep learning for underwater imagery analysis within the recent past. The analysis approaches are categorized according to the object of detection, and the features and deep learning architectures used are highlighted. It is concluded that there is a great scope for automation in the analysis of digital seabed imagery using deep neural networks, especially for the detection and monitoring of seagrass. © Springer International Publishing AG 2017.
AB - Deep learning, also known as deep machine learning or deep structured learning based techniques, have recently achieved tremendous success in digital image processing for object detection and classification. As a result, they are rapidly gaining popularity and attention from the computer vision research community. There has been a massive increase in the collection of digital imagery for the monitoring of underwater ecosystems, including seagrass meadows. This growth in image data has driven the need for automatic detection and classification using deep neural network based classifiers. This paper systematically describes the use of deep learning for underwater imagery analysis within the recent past. The analysis approaches are categorized according to the object of detection, and the features and deep learning architectures used are highlighted. It is concluded that there is a great scope for automation in the analysis of digital seabed imagery using deep neural networks, especially for the detection and monitoring of seagrass. © Springer International Publishing AG 2017.
U2 - 10.1007/978-3-319-70353-4_13
DO - 10.1007/978-3-319-70353-4_13
M3 - Conference paper
SN - 9783319703527
VL - 10617 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 150
EP - 160
BT - International conference on advanced concepts for intelligent vision systems (ACIVS)
A2 - Blanc-Talon, Jacques
A2 - Popescu, Dan
A2 - Scheunders, Paul
A2 - Philips, Wilfried
A2 - Penne, Rudi
PB - Springer
Y2 - 18 September 2017 through 21 September 2017
ER -
