Small Ship Detection on Optical Satellite Imagery with YOLO and YOLT

Wilder Nina, William Condori, Vicente Machaca, Juan Villegas, Eveling Castro

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

5 Citas (Scopus)

Resumen

Actually, the use of deep learning in object detection gives good results, but this performance decreases when there are small objects in the image. In this work, is presented a comparison between the last version of You Only Look Once (YOLO) and You Only Look Twice (YOLT) on the problem of detecting small objects (ships) on optical satellite imagery. Two datasets were used: High-Resolution Ship Collection (HRSC) and Mini Ship Data Set (MSDS), the last one was built by us. The mean object’s width for HRSC and MSDS are 150 and 50 pixels, respectively. The results showed that YOLT is good only for small objects with 76,06% of Average Precision (AP), meanwhile, YOLO reached 69,80% in the MSDS dataset. Moreover, in the case of the HRSC dataset where have objects of different sizes, YOLT obtained a 40% of AP against 75% of YOLO.

Idioma originalInglés
Título de la publicación alojadaAdvances in Information and Communication - Proceedings of the 2020 Future of Information and Communication Conference FICC
EditoresKohei Arai, Supriya Kapoor, Rahul Bhatia
EditorialSpringer
Páginas664-677
Número de páginas14
ISBN (versión impresa)9783030394417
DOI
EstadoPublicada - 2020
EventoFuture of Information and Communication Conference, FICC 2020 - San Francisco, Estados Unidos
Duración: 5 mar. 20206 mar. 2020

Serie de la publicación

NombreAdvances in Intelligent Systems and Computing
Volumen1130 AISC
ISSN (versión impresa)2194-5357
ISSN (versión digital)2194-5365

Conferencia

ConferenciaFuture of Information and Communication Conference, FICC 2020
País/TerritorioEstados Unidos
CiudadSan Francisco
Período5/03/206/03/20

Nota bibliográfica

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

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