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Multi-sensor Data Fusion Using Deep Learning for Bulky Waste Image Classification.
Proc. SPIE 12623 (2023)
Deep learning techniques are commonly utilized to tackle various computer vision problems, including recognition, segmentation, and classification from RGB images. With the availability of a diverse range of sensors, industry-specific datasets are acquired to address specific challenges. These collected datasets have varied modalities, indicating that the images possess distinct channel numbers and pixel values that have different interpretations. Implementing deep learning methods to attain optimal outcomes on such multimodal data is a complicated procedure. To enhance the performance of classification tasks in this scenario, one feasible approach is to employ a data fusion technique. Data fusion aims to use all the available information from all sensors and integrate them to obtain an optimal outcome. This paper investigates early fusion, intermediate fusion, and late fusion in deep learning models for bulky waste image classification. For training and evaluation of the models, a multimodal dataset is used. The dataset consists of RGB, hyperspectral near-infrared (NIR), Thermography, and Terahertz images of bulky waste. The results of this work show that multimodal sensor fusion can enhance classification accuracy compared to a single-sensor approach for the used dataset. Hereby, late fusion performed the best with an accuracy of 0.921 compared to intermediate and early fusion, on our test data.
Impact Factor
Scopus SNIP
Altmetric
0.000
0.235
Anmerkungen
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Publikationstyp
Artikel: Journalartikel
Dokumenttyp
Wissenschaftlicher Artikel
Schlagwörter
Cnn ; Data Fusion ; Early Fusion ; Image Classification ; Intermediate Fusion ; Late Fusion ; Multi-sensor Data ; Multi-stream Model ; Multimodal Data ; Multispectral Data
Sprache
englisch
Veröffentlichungsjahr
2023
HGF-Berichtsjahr
2023
ISSN (print) / ISBN
0277-786X
e-ISSN
1996-756X
Zeitschrift
Proceedings of SPIE
Quellenangaben
Band: 12623
Verlag
SPIE
Begutachtungsstatus
Peer reviewed
Institut(e)
Helmholtz AI - KIT (HAI - KIT)
Scopus ID
85173451638
Erfassungsdatum
2023-10-18