Abstract
Target recognition is an important aspect of air traffic management, but the study on automatic aircraft identification is still in the exploratory stage. Rapid aircraft processing and accurate aircraft type recognition remain challenging tasks due to the high-speed movement of the aircraft against complex backgrounds. Active learning, as a promising research topic of machine learning in recent decades, can use less labeled data to obtain the same model accuracy as supervised learning, which greatly reduces the cost of labeling a dataset. Instead of manually developing policies of accessing the labels of desired instances, an improved active learning approach, which can not only learn to classify samples using small supervision but additionally capture a relatively optimal label query strategy, was developed by employing the reinforcement learning in the process of decision-making. The proposed model was first tested with the Amsterdam Library of Object Images (ALOI) dataset and then used to perform aircraft type recognition on one-month real-world flight track data. Our method offers a satisfactory solution for learning new concepts rapidly from a small amount of data, which well meets the needs of aircraft type recognition task in practical application.
Highlights
With the rapid increase in the variety and quantity of aircraft, precise identification of aircraft types is an important task of air traffic control in daily life and a vital military mission
2) RESULTS Here we represent two experimental results of our model on the Amsterdam Library of Object Images (ALOI) dataset. Both active one-shot learning (AOL) [20] and reinforcement one-shot active learning (ROAL) model were tested on the task in Fig. 1 with the same parameters set-up
3) RESULTS In Fig. 6 and Fig. 7 we report the results of our active model on aircraft type recognition task
Summary
With the rapid increase in the variety and quantity of aircraft, precise identification of aircraft types is an important task of air traffic control in daily life and a vital military mission. Aircraft type recognition methods are still in the exploratory stage, and mature aircraft recognition theories and systems have not yet been formed. In order to achieve better recognition accuracy, aircraft type recognition work still requires substantial human input. The use of machine learning methods to reduce the workload of human experts in aircraft type recognition tasks has become a meaningful research direction. For many real-world tasks, labeled data are scarce whereas unlabeled data are abundant [2]. As is widely acknowledged in this domain, formulating labels is a straightforward
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
