首页|X-ray security inspection for real-world rail transit hubs: a wide-ranging dataset and detection model with incremental learning block
X-ray security inspection for real-world rail transit hubs: a wide-ranging dataset and detection model with incremental learning block
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NETL
NSTL
Springer Nature
Abstract Security inspection plays a crucial role in maintaining public safety, showcasing significant prospects in the automated and accurate identification of prohibited items in X-ray images. However, detection methods based on deep learning models rely on large-labeled datasets and can only achieve closed-set detection with a limited number of categories. Existing public datasets suffer from poor image quality and are limited to airport security, rendering them unsuitable for railway transportation security inspections. To fill this gap, we contributed the first prohibited items detection dataset based on real-world inspection scenarios at rail transit hubs. It includes 5,923 X-ray images, in which seven categories of 10,224 instances are manually annotated by professional security inspectors. In addition, we propose an incremental approach to open-set detection that allows the detection system to be updated online. The experimental results show that our method achieves state-of-the-art performance on different dataset and is able to detect newly added categories online. The impact of different loss functions on the model’s detection performance is also discussed in this paper.
Xizhuo Yu、Chaojie Fan、Jiandong Pan、Guoliang Xiang、Chunyang Chen、Tianjian Yu、Yong Peng、Hanwen Deng
NETL
NSTL
Springer Nature
