Detection of threat objects concealed in passenger clothing and baggage poses a challenge to aviation security. At present, the detection technology is capable of detecting the presence of threats from the scanned images yet requires the involvement of human in determining what type of threat and where it is located. Deep learning-based object detection technique has not been successfully implemented to detect threats in the security screening processes. This research aims to improve the accuracy and the processing time of threat detection in security screening. Enhanced faster region-based convolutional neural network (faster R-CNN) with improved region proposals is used for better threat localization. The proposed system consists of an improved region proposal network that outputs object’s region proposals with an object score to the detector module to accurately locate the threat in the human body. Furthermore, this system uses a modified loss function that strengthens the classification loss. Results obtained by the proposed model show a 15% improvement in object localization. Therefore, the enhanced faster R-CNN achieves an overall detection accuracy of 0.27 in terms of average precision and reduces the processing time by 0.19 s. The results obtained by the enhanced faster R-CNN for detection accuracy are superior to the state-of-the-art system. Also, this model focuses on localizing the threat and identifying its type, which makes the model suitable for threat detection security screening. Besides, the research also addresses the time consumption issue in detecting the threat object.

中文翻译：

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一种新颖的增强区域提议网络和改进的损失函数：使用深度学习进行安全筛选中的威胁对象检测

检测隐藏在旅客衣服和行李中的威胁物体对航空安全构成了挑战。目前，检测技术能够从扫描图像中检测到威胁的存在，但需要人工参与确定威胁的类型和位置。基于深度学习的对象检测技术尚未成功实施以检测安全筛选过程中的威胁。本研究旨在提高安检中威胁检测的准确性和处理时间。具有改进的区域提议的增强的基于区域的更快卷积神经网络（更快的 R-CNN）用于更好的威胁定位。所提出的系统由一个改进的区域提议网络组成，该网络将带有对象分数的对象区域提议输出到检测器模块，以准确定位人体中的威胁。此外，该系统使用改进的损失函数来加强分类损失。所提出的模型获得的结果显示，对象定位有 15% 的改进。因此，增强的faster R-CNN在平均精度方面实现了0.27的整体检测精度，并将处理时间减少了0.19秒。由增强型 Faster R-CNN 获得的检测精度结果优于最先进的系统。此外，该模型侧重于定位威胁和识别其类型，这使得该模型适用于威胁检测安全筛选。除了，