An optimal defect recognition security-based terahertz low resolution image system using deep learning network

Abstract

The physics of Terahertz (THz) technology is the electromagnetic (EM) spectrum band between the infrared and the microwave band with frequencies of about 0.1 to 30 THz. THz signals have gained adoption in medicine, telecommunications, security monitoring and imaging. THz imaging technology has the advantages of rapid imaging, strong penetration, and harmless to the human body hence widely used in a variety of security environments and has become an alternative technology for X-ray imaging. However, THz is characterized by low resolution of THz images of which noise is an integral factor constituting a defect. Clarity of THz image is therefore essential at various security checkpoints to avoid life’s dangers and treats. In this paper, we propose an efficient and high-performance defect detection model based on RetinaNet to recognize defects from captured images. The strategy of transfer learning is introduced to improve detection performance accuracy, which enhances the average precision (AP) by 19.2%. Contrary to existing THz image detection techniques on image recognition pertaining to the whole region of the image, we adopt a different approach via differential evolution search algorithm for optimization given the small proportion of defect area which improves the AP by 9.9% comparing with the fine-tuned model. For the problem of the lack of defect data samples, image augmentation is adopted to enrich our training samples, which improves the AP by 9.5%. As for the problem of low precision and recall in detecting blurred images, we firstly manually generate clear-blurred image pairs to train a GAN. Then, the blurred images are deblurred using a trained generator. We get 5.5% AP improvement on the testset using our approach. Compared with existing works, the optimized model based on RetinaNet has better detection performance, subsequently, proving the practicability and effectiveness of the proposed method.