The development of phase-based techniques has accelerated the pace of X-ray imaging. Darkfield images are sensitive to length-scale inhomogeneities below the spatial resolution of the system, and phase-contrast images are improved for detailed visibility. A new technique to X-ray suitcases to detect trace amounts of explosives has been developed by a team of researchers from University College London, Nylers Ltd. and XPCI Technology Ltd. And how combining it with conventional attenuation improves the ability to discriminate threat agents. They also published research in the journal Nature Communications, including adapting his conventional X-ray detector and using deep learning applications to better detect toxic chemicals in luggage.
Moreover, their work shows that using different energy dependencies of darkfield and decay signals can dispel lingering misconceptions. Additionally, two proof-of-concept experiments have shown that darkfield textures are suitable for identification using machine learning techniques. Applying the same method to datasets with darkfield images removed resulted in poor performance. Previous studies have demonstrated that the type of material has a significant effect on the microbending that occurs when interacting with X-rays. Researchers aimed to take advantage of these bends to build an accurate X-ray system.
The first change the researchers made to an existing X-ray machine was to add a box containing a mask, a sheet of metal with small holes punched in it. The purpose of the mask is to split the x-ray beam into many smaller beams. A deep learning AI application was then fed scan results from various explosive-embedded objects. The goal was to teach the machine how to recognize the appearance of minute bends in such materials. After the machine was trained, they tested its capabilities by scanning other items for implanted bombs. I discovered that
The device successfully identified bends as small as 1 microradian (1/20,000th of a degree). However, because the study was small, there is still room for further investigation. The overall results demonstrate the potential for combined use of deep neural networks and darkfield in non-security applications. In addition to being useful for transportation security personnel, the team believes that method could be slightly modified for use in other applications, such as medicine. They suspect they may be trained to find malignant tumors too small to be detected by standard laboratory equipment, or small scratches on the surfaces of buildings and planes.
This Article is written as a research summary article by Marktechpost Staff based on the research paper 'Enhanced detection of threat materials by dark-field x-ray imaging combined with deep neural networks'. All Credit For This Research Goes To Researchers on This Project. Check out the paper and reference article. Please Don't Forget To Join Our ML Subreddit
Khushboo Gupta is a consulting intern at MarktechPost. She is currently pursuing her B.Tech at the Indian Institute of Technology (IIT), Goa. She has her passions in the fields of machine learning, natural language processing, and her web development. She enjoys learning more about the technical field by participating in some challenges.
