Abstract
For personal safety and crime prevention, some research studies based on deep learning have achieved success in the object detection of X-ray security inspection. However, the research on dangerous liquid detection is still scarce, and most research studies are focused on the detection of some prohibited and common items. In this paper, a lightweight dangerous liquid detection method based on the Depthwise Separable convolution for X-ray security inspection is proposed. Firstly, a dataset of seven common dangerous liquids with multiple postures in two detection environments is established. Secondly, we propose a novel detection framework using the dual-energy X-ray data instead of pseudocolor images as the objects to be detected, which improves the detection accuracy and realizes the parallel operation of detection and imaging. Thirdly, in order to ensure the detection accuracy and reduce the computational consumption and the number of parameters, based on the Depthwise Separable convolution and the Squeeze-and-Excitation block, a lightweight object location network and a lightweight dangerous liquid classification network are designed as the backbone networks of our method to achieve the location and classification of the dangerous liquids, respectively. Finally, a semiautomatic labeling method is proposed to improve the efficiency of data labeling. Compared with the existing methods, the experimental results demonstrate that our method has better performance and wider applicability.
Highlights
At present, nondestructive testing technology has been widely applied in various fields [1,2,3,4], among which the application of X-ray detection technology in airports, customs, railway stations, and other transportation departments reduces criminal behavior effectively
In order to achieve a good balance between the number of parameters, computational consumption, and detection accuracy for our detection task, we propose a lightweight dangerous liquid detection method for X-ray security inspection (DLDX) with higher accuracy, fewer parameters, and less computational consumption
We trained our lightweight object location network on the manually labeled dataset for comparison with our method. e results are given in Table 3. e results show the mIOU of the trained lightweight object location network with our semiautomatic labeling is only 0.011 lower than the network trained on manually labeled dataset. e small gap is entirely acceptable. erefore, our semiautomatic labeling method is effective
Summary
Nondestructive testing technology has been widely applied in various fields [1,2,3,4], among which the application of X-ray detection technology in airports, customs, railway stations, and other transportation departments reduces criminal behavior effectively. The technology requires security inspectors to determine whether prohibited items are hidden in baggage. The passing frequency of baggage increases greatly, and the security inspectors have to complete the detection in a very short time. Manual detection has been widely used in the field of X-ray security detection, but this method mainly relies on the experience of security inspectors. A fast and effective automatic object detection method for X-ray security inspection is significant
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