Improved SSD network for fast concealed object detection and recognition in passive terahertz security images.
Improved SSD network for fast concealed object detection and recognition in passive terahertz security images.
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DOI：
10.1038/s41598-022-16208-0
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发表时间：
2022-07-15
影响因子：
4.6
通讯作者：
Fang, Guangyou
中科院分区：
文献类型：
--
作者：
Cheng, Lu;Ji, Yicai;Li, Chao;Liu, Xiaojun;Fang, Guangyou
随着全球反恐措施的加强，在公共场所进行安全检查，以发现人体携带的隐藏物体越来越重要。近年来的研究表明，深度学习有助于检测被动太赫兹图像中的隐藏物体。然而，以前的研究未能实现实时标记的上级准确性和性能。我们的研究旨在提出一种新的方法，准确和实时检测隐藏的物体在太赫兹图像。为了实现这一目标，我们使用被动太赫兹设备收集的人体图像数据，训练和测试了一种基于深度残差网络的有前途的检测器。具体来说，我们将SSD（Single Shot MultiBox Detector）算法的骨干网络替换为更具代表性的残差网络，以降低网络训练的难度。针对太赫兹图像中小目标的重复检测和漏检问题，提出了一种基于特征融合的太赫兹图像目标检测算法。此外，我们在SSD中引入了混合注意机制，以提高算法获取目标细节和位置信息的能力。最后，引入Focal Loss函数，提高了模型的鲁棒性。实验结果表明，SSD算法的准确率从95.04%提高到99.92%。与Faster RCNN、YOLO和RetinaNet等当前主流模型相比，该方法可以在更快的速度下保持较高的检测精度。该方法在验证子集上的平均精度为99.92%，F1得分为0.98，预测速度为17 FPS。本文提出的基于SSD-ResNet-50的方法可以为深度学习技术在太赫兹智能安防系统中的应用和发展提供技术参考。未来可以广泛应用于一些有实时安检需求的公共场景。
With the strengthening of global anti-terrorist measures, it is increasingly important to conduct security checks in public places to detect concealed objects carried by the human body. Research in recent years has shown that deep learning is helpful for detecting concealed objects in passive terahertz images. However, previous studies have failed to achieve superior accuracy and performance for real-time labeling. Our research aims to propose a novel method for accurate and real-time detection of concealed objects in terahertz images. To reach this goal we trained and tested a promising detector based on deep residual networks using human image data collected by passive terahertz devices. Specifically, we replaced the backbone network of the SSD (Single Shot MultiBox Detector) algorithm with a more representative residual network to reduce the difficulty of network training. Aiming at the problems of repeated detection and missed detection of small targets, a feature fusion-based terahertz image target detection algorithm was proposed. Furthermore, we introduced a hybrid attention mechanism in SSD to improve the algorithm’s ability to acquire object details and location information. Finally, the Focal Loss function was introduced to improve the robustness of the model. Experimental results show that the accuracy of the SSD algorithm is improved from 95.04 to 99.92%. Compared with other current mainstream models, such as Faster RCNN, YOLO, and RetinaNet, the proposed method can maintain high detection accuracy at a faster speed. This proposed method based on SSD achieves a mean average precision of 99.92%, an F1 score of 0.98, and a prediction speed of 17 FPS on the validation subset. This proposed method based on SSD-ResNet-50 can provide a technical reference for the application and development of deep learning technology in terahertz smart security systems. In the future, it can be widely used in some public scenarios with real-time security inspection requirements.
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