Naslov
Hand-Crafted Features for Floating Plastic Detection

Autori
Sukno, Matija ; Palunko, Ivana

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) / - : Institute of Electrical and Electronics Engineers (IEEE), 2022, 3378-3383

ISBN
978-1-6654-7927-1

Skup
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Mjesto i datum
Kyoto, Japan, 23.10.2022. - 27.10.2022

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Sea surface ; Memory management , Wildlife , Detectors , Object detection , Feature extraction , Plastics
(Sea surface , Memory management , Wildlife , Detectors , Object detection , Feature extraction , Plastics)

Sažetak
Plastic waste is a global concern that has a negative impact on the oceans and wildlife health. This paper focuses on detection of floating plastics in aerial images taken from unmanned aerial vehicles (UAVs). It proposes a new method for plastic detection in marine environments, based on SIFT descriptor and color histograms for feature extraction, as an alternative to state-of- the-art object detectors based on convolutional neural networks (CNNs), Our approach is named SURFACE: “SIFT featURes For plAstiC dEtection”. We investigate how different color-spaces and image resolutions impact the extraction of SIFT features and compare SURFACE to ResNet CNN. Also, we provide a detailed comparison with YOLO and Faster-RCNN object detection models and show that SURFACE achieves approximately the same accuracy while being faster and less memory consuming. The dataset acquired during this research will be publicly available.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo

POVEZANOST RADA