Anomaly detection in Sonar images: application of saliency filters

被引：0

作者：

Bazzarello, Lorenzo ^{[1
]}

Pulpito, Osvaldo ^{[2
]}

Cannarsa, Francesco ^{[1
]}

Bresciani, Matteo ^{[3
]}

Costanzi, Riccardo ^{[3
]}

Acito, Nicola ^{[3
]}

Diani, Marco ^{[4
]}

Corsini, Giovanni ^{[3
]}

Caiti, Andrea ^{[5
]}

机构：

[1] Italian Navy, CSSN La Spezia, La Spezia, Italy

[2] Italian Navy, Electon Warfare, CSSN, ITE Livorno, Livorno, Italy

[3] Univ Pisa, Dept Informat Engn, Pisa, Italy

[4] Italian Navy, Naval Acad, Livorno, Italy

[5] Univ Pisa, E Piaggio Res Ctr, Dept Informat Engn, Pisa, Italy

来源：

2022 IEEE INTERNATIONAL WORKSHOP ON METROLOGY FOR THE SEA LEARNING TO MEASURE SEA HEALTH PARAMETERS (METROSEA) | 2022年

关键词：

Mine Warfare; Image Processing; Saliency Detection; Autonomous Underwater Vehicle; Side Scan Sonar; MODEL;

D O I：

10.1109/METROSEA55331.2022.9950937

中图分类号：

P75 [海洋工程];

学科分类号：

0814 ; 081505 ; 0824 ; 082401 ;

摘要：

Sea mines are still a concrete menace both for military and civilian ships and detecting them is necessary to ensure the safety of the navigation. In this work we explored the possibility of automatically detect mines by using unmanned Autonomous Underwater Vehicles equipped with Side Scan Sonar (SSS) Sensors. To accomplish the detection task, we considered saliency detection algorithms coming from RGB and radar fields to highlight the mines with respect to the background. The algorithms were tested on a valuable dataset of images collected by the Italian Navy under operational conditions during several activities conducted in the Mediterranean Sea. We evaluated the performance according to broadly used performance indices such as ROC curves and MAE scores. Furthermore, a new performance analysis score called FAR@95%Pd is presented.

引用

页码：220 / 224

页数：5

共 11 条

[1] Comparative analysis of clutter removal techniques over experimental IR images [J].