A neural network-based system designed for automated detection of concealed items using a postal terahertz scanner is presented, with system optimization provided. A dataset of objects scanned by a THz scanner is introduced. A convolutional neural network is trained on this dataset of terahertz images to classify and detect whether an image contains a prohibited item or not. The system is tested using real-world samples, achieving an accuracy of 95.5% mAP@0.5. The results demonstrate the effectiveness of employing a neural network in postal terahertz scanners and its potential for use in security and surveillance applications. |
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Object detection
Terahertz radiation
Scanners
Education and training
Image processing
Optical engineering
Body scanners