Passive terahertz image segmentation algorithm

Xin, 张馨 Zhang; Yuanmeng, 赵源萌 Zhao; Cunlin, 张存林 Zhang

doi:10.3788/hplpb20132506.1597

Cited by 5 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [8], according to the characteristics of the terahertz image, the Otsu threshold was used to segment the threshold, the contour tracking method was applied to extract the contours, and object detection in the human terahertz image was achieved. In [10], a clustering algorithm based on fuzzy local information C-Means was used to detect objects in human terahertz images.…”

Section: Introductionmentioning

confidence: 99%

R-PCNN Method to Rapidly Detect Objects on THz Images in Human Body Security Checks

Hong

Zhang

Wang

et al. 2018

2018 IEEE SmartWorld, Ubiquitous Intelligence &Amp; Computing, Advanced &Amp; Trusted Computing, Scalable Computing &Amp; Commu

View full text Add to dashboard Cite

Terahertz human body security images have low resolution and a low signal-to-noise ratio. In the traditional method, image segmentation, positioning, and identification are applied to detect objects carried by humans in the THz images. However, it is difficult to satisfy the requirements of detection accuracy and speed with this approach. The current research presents a faster detection framework (R-PCNN) 1 combining the preprocessing and structure optimization of Faster R-CNN. The experiment results show that this method can effectively improve the accuracy and speed of object detection in human body THz images. A detection accuracy of 84.5% can be achieved in dense flow scenes, with an average detection time of less than 20 milliseconds for each image.

show abstract

Section: Introductionmentioning

confidence: 99%

R-PCNN Method to Rapidly Detect Objects on THz Images in Human Body Security Checks

Hong

Zhang

Wang

et al. 2018

2018 IEEE SmartWorld, Ubiquitous Intelligence &Amp; Computing, Advanced &Amp; Trusted Computing, Scalable Computing &Amp; Commu

View full text Add to dashboard Cite

show abstract

Concealed hazardous object detection for terahertz images with cross-feature fusion transformer

Zeng,

Wu,

Chen

et al. 2024

Optics and Lasers in Engineering

View full text Add to dashboard Cite

Improved SSD network for fast concealed object detection and recognition in passive terahertz security images

Cheng

et al. 2022

Sci Rep

View full text Add to dashboard Cite

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.

show abstract

Passive terahertz image segmentation algorithm

Cited by 5 publications

References 0 publications

R-PCNN Method to Rapidly Detect Objects on THz Images in Human Body Security Checks

R-PCNN Method to Rapidly Detect Objects on THz Images in Human Body Security Checks

Concealed hazardous object detection for terahertz images with cross-feature fusion transformer

Improved SSD network for fast concealed object detection and recognition in passive terahertz security images

Contact Info

Product

Resources

About