| Popis: |
Explosive detection is crucial for public safety and confidence. Among various solutions for this purpose, hyperspectral imaging differs from its alternatives with its detection capability from standoff distances. However, the state-of-the-art for such a technology is still significantly missing a complete technical and experimental framework for surveillance applications. In this article, an end-to-end technical framework, which involves capturing, preprocessing, reflectance conversion, target detection, and performance evaluation stages, is proposed to reveal the potential of a ground-based hyperspectral image (HSI) surveillance system for the detection of explosive traces. The proposed framework utilizes a short-wave infrared region (0.9–1.7 μm), which covers the distinctive absorption characteristics of different explosives. Three classes of detection methods, namely index, signature, and learning-based methods are adapted to the proposed surveillance system. Their performances are compared over various experiments, which are specifically designed for granular and sprayed residues, fingerprint residues, and explosive traces on vehicles. The experiments reveal that the best method in terms of precision and recall performances is hybrid structure detector, which effectively combines signature-based detection with unmixing. While deep-learning-based methods have also achieved satisfactory precision values, their low recall values for the moment have comparatively limited their usage for the high-risk cases. Although one of the main reasons for the current performances of deep-learning methods is less data for learning, these performances for HSIs can be increased with more data in the future as in other image applications. |
