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Threat detection capabilities are being revolutionized by the integration of AI and ML algorithms into explosive detection systems. Large volumes of data can be analyzed by systems in real time, which increase detection accuracy while lowering false alarm rates. Additionally, AI-driven algorithms can adjust and pick up on novel threat patterns, improving the efficacy and reactivity of explosive detectors. Governments and manufacturers are heavily investing in the development of advanced AI/ML-based detection algorithms for identifying new threats.
For instance, In March 2021, Physical Sciences Inc. (PSI) and Alakai Defense Systems, Inc. (Alakai) were each awarded a $1 million, 24-month SBIR Phase II contract. These awards further lean into the goal of developing advanced AI/ML-based detection algorithms that can shorten the timeline for deployment of capabilities able to identify threats in the field.
The explosive detectors market is seeing an increase in the use of multi-modal explosive detection systems, which integrate various detection methods like thermal analysis, X-ray imaging, and trace detection. Complementary features of these integrated systems enable thorough screening of both bulk and trace explosives in a variety of settings. These systems improve security outcomes by reducing the possibility of false alarms and increasing overall detection sensitivity by utilizing various detection modalities.
For instance, in October 2022, Smiths Detection launched the HI-SCAN 7555 DV, a dual-view X-ray scanner designed for automatic explosives detection and enhanced security screening capabilities. The HI-SCAN 7555 DV offers several key features that enhance its performance and efficiency. The HI-SCAN 7555 DV is designed to provide efficient screening in high threat applications, such as airports, urban security, and ports & borders checkpoints, while ensuring high levels of security and automation without requiring additional hardware.