Terahertz Image Detection with the Improved Faster Region-Based Convolutional Neural Network

In recent years, terahertz imaging systems and techniques have been developed and have gradually become a leading frontier field. With the advantages of low radiation and clothing-penetrable, terahertz imaging technology has been widely used for the detection of concealed weapons or other contraband carried on personnel at airports and other secure locations. This paper aims to detect these concealed items with deep learning method for its well detection performance and real-time detection speed. Based on the analysis of the characteristics of terahertz images, an effective detection system is proposed in this paper. First, a lots of terahertz images are collected and labeled as the standard data format. Secondly, this paper establishes the terahertz classification dataset and proposes a classification method based on transfer learning. Then considering the special distribution of terahertz image, an improved faster region-based convolutional neural network (Faster R-CNN) method based on threshold segmentation is proposed for detecting human body and other objects independently. Finally, experimental results demonstrate the effectiveness and efficiency of the proposed method for terahertz image detection.

AAGC-Autonomous Armed Guard Companion

Technology has revolutionized human world over the past centuries. The world has started to move towards the autonomous world. However, in terms of security and safety of humans from antisocial threat person, technology has not achieved its peak in providing autonomous system. Places such as airport, railways stations, and malls are well secured either by metal detectors or by manual scrutiny. Moreover, the current scenario demands a very high security in common places such as hospital, schools, and community halls where manual security checks might not be possible. In this work an autonomous system is developed to detect and track concealed weapons with potential to stop the threat person before causing any harm or danger. The system achieves its task by carrying out three main functions in a sequential order. A microwave sensor and metal detector together detects the concealed weapon by means of the Doppler shift in the radiation and eddy currents induced in the metal respectively. Secondly, the output voltage from the microwave senor is processed by a microcontroller. Lastly, a DC motor attached with a laser pointer, receives input from microcontroller such that rotates in a direction pointing the concealed weapon.

Design Considerations in the Proliferation of Police Body-Worn Cameras

More and more police departments are equipping their officers with body-worn cameras. To maximize the utility of body cams, designers have considered issues such as camera-mounting position, camera-mount stability, methods of activation, and data transfer methods. The human factors/ergonomics community can make important contributions to the design of body-worn cameras and identify and address issues that could arise from the introduction of new technologies (e.g., biometric identification and automatic detection of concealed weapons). Engaging with this ever-expanding technology will benefit law enforcement agencies and the communities they serve and protect.