Citation

Kowalski M, Pałka N, Młyńczak J, Karol M, Czerwińska E, Życzkowski M, Ciurapiński W, Zawadzki Z, Brawata S. Sensors (Basel) 2021; 21(16).

Copyright

(Copyright © 2021, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/s21165330

PMID

unavailable

Abstract

Smuggling of drugs and cigarettes in small inflatable boats across border rivers is a serious threat to the EU's financial interests. Early detection of such threats is challenging due to difficult and changing environmental conditions. This study reports on the automatic detection of small inflatable boats and people in a rough wild terrain in the infrared thermal domain. Three acquisition campaigns were carried out during spring, summer, and fall under various weather conditions. Three deep learning algorithms, namely, YOLOv2, YOLOv3, and Faster R-CNN working with six different feature extraction neural networks were trained and evaluated in terms of performance and processing time. The best performance was achieved with Faster R-CNN with ResNet101, however, processing requires a long time and a powerful graphics processing unit.

Language: en

Keywords

deep learning; automatic object detection; neural networks; thermal infrared