Ship detection in thermal infrared using paired visible light images and domain adaptation via knowledge distillation

Abstract

Electro-optical (EO) sensors are essential for surveillance in military and security applications. Recent technological advancements, especially the developments in Deep Learning (DL), have enabled improved object detection and tracking in complex and dynamic environments. Most of this research focuses on readily available visible light (VIS) images. To apply these technologies for Thermal infrared (TIR) imagery, DL networks can be retrained using image data in the TIR domain. However, such a training set with enough samples is not easily available. This paper presents an unsupervised domain adaptation method for ship detection in TIR imagery using paired VIS and TIR images. The proposed method leverages on the pairing of VIS and TIR images and performs domain adaptation using detections in the VIS imagery as ground-truth to provide data for the TIR domain learning. The method performs ship detection from the VIS images using a pretrained convolutional neural network (CNN). These detections are subsequently improved using a tracking algorithm. The proposed TIR object detection model follows a two-stage training process. In the first stage, the model's head is trained, which consists of the regression layers that output the bounding boxes of the detected objects. In the second stage, the model's feature extractor is trained to learn more discriminative features. The method is evaluated on a dataset of recordings at Rotterdam harbor. Experiments demonstrate that the resulting TIR detector performs comparably with its VIS counterpart, in addition to providing reliable detections in adverse environmental conditions where VIS model fails. The proposed method has significant potential for real-world applications, including maritime surveillance.