Multiple Object Detection Based on Clustering and Deep Learning Methods

Multiple object detection is challenging yet crucial in computer vision. In This study, owing to the negative effect of noise on multiple object detection, two clustering algorithms are used on both underwater sonar images and three-dimensional point cloud LiDAR data to study and improve the perform...

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Bibliographic Details
Published inSensors (Basel, Switzerland) Vol. 20; no. 16; p. 4424
Main Authors Nguyen, Huu Thu, Lee, Eon-Ho, Bae, Chul Hee, Lee, Sejin
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 07.08.2020
MDPI
Subjects
Algorithms
Cameras
Classification
Clustering
Computer vision
DBSCAN
Deep learning
K-means clustering
LiDAR
multiple object detection
Neural networks
Outliers (statistics)
Performance enhancement
Sensors
underwater sonar images
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Summary:Multiple object detection is challenging yet crucial in computer vision. In This study, owing to the negative effect of noise on multiple object detection, two clustering algorithms are used on both underwater sonar images and three-dimensional point cloud LiDAR data to study and improve the performance result. The outputs from using deep learning methods on both types of data are treated with K-Means clustering and density-based spatial clustering of applications with noise (DBSCAN) algorithms to remove outliers, detect and cluster meaningful data, and improve the result of multiple object detections. Results indicate the potential application of the proposed method in the fields of object detection, autonomous driving system, and so forth.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1424-8220
1424-8220
DOI:10.3390/s20164424