Detection of Inflatable Boats and People in Thermal Infrared with Deep Learning Methods

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.

Opto-Electronics Review

“…In order to study the thermal behaviour of samples, a thermographic camera comes as a suitable solution. The possibility of using thermal imaging has been widely used in aerial thermography [6], aerospace industry [7], automotive industry [8], chemical [9], electrical and electronical industry [10], material testing [11], and security [12]. However, in the field of solar cells the thermal imaging is mainly used to evaluate the system efficiency losses on a photovoltaic installation to determine the actual health status [13][14][15].…”

Tracking of objects in a multi-sensor fusion system for border surveillance

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Border surveillance has been a topic with increasing interest in the last few years [1][2][3], particularly given the fact that in the last years irregular migration has dramatically increased [4]. For border guards, the main concern remains to detect an illegal border crossing as soon as possible.

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