Marine Mine Detection Using Deep Learning

被引：0

作者：

Diana, Moina ^{[1
]}

Munteanu, Dan ^{[1
]}

Cristea, Dragos Sebastian ^{[1
]}

Munteanu, Nicoleta ^{[2
]}

机构：

[1] Duneirea Jos Univ Galati, Galati, Romania

[2] Childrens Palace, Galati, Romania

来源：

2022 26TH INTERNATIONAL CONFERENCE ON SYSTEM THEORY, CONTROL AND COMPUTING (ICSTCC) | 2022年

关键词：

floating and underwater mine detection; deep learning; synthetic image; object recognition;

D O I：

10.1109/ICSTCC55426.2022.9931775

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The paper addresses the detection of floating and underwater marine mines from images recorded from cameras (taken from drones, submarines, ships, boats). Due to the lack of image datasets, images were taken from the Internet and by using the technique of augmentation and synthetic image generation (by overlapping images with different types of mines over water backgrounds) 2 data sets were built (one for floating mines and one for underwater mines). The networks were trained and compared using 3 types of Deep Learning models Yolov5, SSD and EfficientDet (Yolov5, SSD for floating mines and Yolov5 and EfficientDet for underwater mines). The networks were also tested in the context of an IoT device (RaspberryPi 4, RPi camera).

引用

页码：237 / 243

页数：7

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