Deep Domain Adaptation for Single-Shot Vehicle Detector in Satellite Images

Koga, Yohei; Miyazaki, Hiroyuki; Shibasaki, Ryosuke

doi:10.1109/igarss.2018.8519129

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…Through training, the domain classifier finds it harder to distinguish between domains as the features learnt become more invariant to changes in domain. In papers, [17][18][19][20] similar routes are taken. The authors of paper 21 use multiple adversarial discriminators to make use of complex structures of the target domain and improve performance.…”

Section: Related Workmentioning

confidence: 99%

Multimodal object detection using unsupervised transfer learning and adaptation techniques

Abbott

Robertson

Rincón

et al. 2019

Artificial Intelligence and Machine Learning in Defense Applications

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Deep neural networks achieve state-of-the-art performance on object detection tasks with RGB data. However, there are many advantages of detection using multi-modal imagery for defence and security operations. For example, the IR modality offers persistent surveillance and is essential in poor lighting conditions and 24hr operation. It is, therefore, crucial to create an object detection system which can use IR imagery. Collecting and labelling large volumes of thermal imagery is incredibly expensive and time-consuming. Consequently, we propose to mobilise labelled RGB data to achieve detection in the IR modality. In this paper, we present a method for multi-modal object detection using unsupervised transfer learning and adaptation techniques. We train faster RCNN on RGB imagery and test with a thermal imager. The images contain object classes; people and land vehicles and represent real-life scenes which include clutter and occlusions. We improve the baseline F1-score by up to 20% through training with an additional loss function, which reduces the difference between RGB and IR feature maps. This work shows that unsupervised modality adaptation is possible, and we have the opportunity to maximise the use of labelled RGB imagery for detection in multiple modalities. The novelty of this work includes; the use of the IR imagery, modality adaption from RGB to IR for object detection and the ability to use real-life imagery in uncontrolled environments. The practical impact of this work to the defence and security community is an increase in performance and the saving of time and money in data collection and annotation.

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Section: Related Workmentioning

confidence: 99%

Multimodal object detection using unsupervised transfer learning and adaptation techniques

Abbott

Robertson

Rincón

et al. 2019

Artificial Intelligence and Machine Learning in Defense Applications

View full text Add to dashboard Cite

show abstract

Fault diagnosis algorithm based on GADF-DFT and multi-kernel domain coordinated adaptive network

Yin,

Jiang,

Wang

et al. 2024

J. vibroeng.

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To address the problems of low detection accuracy of rolling bearings under different loads and the difficulty of effectively identifying the lack of labelled data, a rolling bearing fault diagnosis method combining GADF-DFT image coding and Multi-kernel domain coordinated adaptation network is proposed. Firstly, the vibration signal is converted into a two-dimensional image using GADF coding technology, and then the GADF image is converted into the frequency domain using discrete Fourier transform to extract deeper feature information. Combined with the multi-source domain adaptive method, the public feature extraction module is used to initially achieve feature mining of the image; the MK-MMD algorithm of the domain-specific adaptive module reduces the difference in feature distribution between the source and target domains; and the final classification difference minimization module reduces the problems caused by the classification errors that may be generated by the different domain classifiers due to the fact that the data samples are located near the category boundaries. The test uses the Case Western Reserve University dataset and divides the dataset with different operating conditions as the source and target domains, and the test results show that the proposed model demonstrates its effectiveness in responding to the complex operating condition changes in rolling bearing fault detection in multiple operating condition migration tasks, good adaptability and robustness, and is able to achieve accurate fault diagnosis under different operating conditions.

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Deep Domain Adaptation for Single-Shot Vehicle Detector in Satellite Images

Cited by 2 publications

References 8 publications

Multimodal object detection using unsupervised transfer learning and adaptation techniques

Multimodal object detection using unsupervised transfer learning and adaptation techniques

Fault diagnosis algorithm based on GADF-DFT and multi-kernel domain coordinated adaptive network

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