Home /Research /Object Detection using Transfer Learning for Underwater Robot
PERCEPTION

Object Detection using Transfer Learning for Underwater Robot

Chia-Chin Wang, Hooman Samani

Year
2020
Citations
15

Abstract

In this paper the usage of Transfer Learning method for object detection in underwater environment is experienced and evaluated. Deep learning method of YOLO is utilized for detection of different types of fish underwater. A ROV equipped with camera is employed for video streaming underwater and the data has been analyzed on the main computer Our experimental results confirmed improvement in the mAP by 4% using transfer learning.

Keywords

UnderwaterComputer scienceTransfer of learningArtificial intelligenceObject detectionRemotely operated underwater vehicleComputer visionObject (grammar)RobotTransfer (computing)

Related papers

Browse all PERCEPTION papers