A classification framework for concealed weapon detection using sub-terahertz images: LSTM-based optimized deep neural network

Terahertz imaging technology is a promising method for preventing terrorist attacks due to its ability to penetrate various materials and reveal hidden objects without emitting harmful radiation. This study proposes a fast classification framework with high detection accuracy for terahertz imaging systems that can be used to detect hidden weapons on people at airports and other checkpoints. The proposed framework is a hybrid of a deep neural network based on Color Layout Filter (CLF) and LSTM (Long Short-Term Memory). The performance of this neural network, combined with different optimization methods, was compared with classical machine learning techniques. In the performance evaluation, it was found that the deep neural network model using the Adam optimization algorithm outperformed classical machine learning techniques. This proposed neural network achieved 0.8890 accuracy, 0.8992precision, 0.8890recall, 0.8874F-score, and an ROC area of 0.9675after cross-validation.