Terahertz Imaging Technology Research Articles

Feasibility and potential of terahertz spectral and imaging technology for Apple Valsa canker detection: A preliminary investigation

Apple Valsa canker (AVC) caused by the Ascomycete Valsa mali, seriously constrains the production and quality of apple fruits. The symptomless incubation characteristics of Valsa mali make it highly challenging to detect AVC at an early infection stage. After infecting the wound of apple bark, the pathogenic hyphae of AVC will expand and colonize the phloem tissue. Meanwhile, various enzymes and toxic substances released by hyphae cause the decomposition of cellulose and lignin, and the generation of poisonous secondary metabolites in bark tissue. However, these early symptoms of AVC are invisible from the bark’s appearance. Fortunately, Terahertz Spectral Imaging (ThzSI) technology with the advantage of penetrating, and fingerprinting is promising for detecting hidden or slight symptoms of the fungal infection. This study is a preliminary investigation of terahertz frequency-domain spectra for AVC in the early stage of infection. Healthy and two-week-infected apple tree branches were prepared for capturing ThzS images, and the spectral data were preprocessed by Multivariate scattering correction (MSC), Savitzky-Golay convolution smoothing (SG), and standard normal variate (SNV) respectively to remove data noise and improve data quality. Principal component analysis (PCA), competitive adaptive reweighted sampling (CARS), and random frog (RFROG) were employed to extract the spectral feature bands to eliminate redundant data and improve computational efficiency. Machine learning models were established based on the spectral features to detect AVC at an early infection stage, where 11 of them exhibited the best performance with F1-score of 99.72%. To further explore disease information in spatial spectra, imaging data were acquired using terahertz imaging technology. Based on imaging data, pseudo-color imaging, histogram equalization, and Otsu segmentation were employed to visualize early infection areas in apple barks. Furthermore, histogram feature (HF), shape feature (SF), and local binary pattern (LBP) extracted from terahertz spectral images were utilized to establish the SVM, RF, and KNN models. HF-SF-KNN and HF-SF-LBP-KNN with the best performance achieved F1-score of 98.82%. This study presents a preliminary application of terahertz spectral and imaging technology for early-stage AVC detection and demonstrates its feasibility. Additionally, it provides a new way to detect AVC, which expands the application of ThzSI technology in tree disease detection in orchards and lays the foundation for further research.

Self-supervised learning-based re-parameterization instance segmentation for hazardous object in terahertz image

Recently, terahertz imaging technology has shown widespread potential applications in the public security screening field. Previous terahertz hazardous object detection techniques primarily relied on manual recognition and image processing methods. Some solutions incorporating deep learning technologies depend on large quantities of high-quality data, making it challenging to achieve low-cost and high-performance detection. To solve the issue of dependency on large amounts of high-quality data, we propose a diversified dynamic structure You Only Look Once (DDS-YOLO) model based on masked image modeling and structural reparameterization for the instance segmentation of hazardous objects in terahertz security inspection images. To address the scarcity and annotating difficulty problems of terahertz security inspection image samples, we combine automatic data strategies with masked image modeling for self-supervised learning. We propose a multilevel feature refinement fusion mechanism to enhance the quality of learned feature representations. The backbone parameter transfer and fine-tuning training strategies are employed to achieve hazardous object instance segmentation on the terahertz dataset. To address the low detection accuracy issue caused by the poor terahertz image quality, we develop a reparameterizable hierarchical structure for the backbone, improve a multi-scale feature-integrating neck, and design a dynamically decoupled head with lower computational requirements to enhance the performance of the instance segmentation model. Experimental results demonstrate that the proposed model accurately outputs detection boxes, categories, and segmentation masks for hazardous objects with minimal training samples. The comparative experimental results indicate that the proposed model outperforms existing state-of-the-art methods in terms of detection performance. The proposed DDS-YOLO model achieves 59.3 % in mask mean Average Precision (mAP) and 61.3 % in box mAP, and the model parameters and computational requirements also meet practical application scenarios.

Large depth-of-focus achievement based on an aspheric lens with a ring

Terahertz (THz) imaging technology has been widely studied because of its easy penetration of non-polar materials and low photon energy. To acquire a beam featuring both excellent transverse spatial resolution and a considerable depth-of-focus (DOF) to fulfill the demands of two-dimensional and three-dimensional THz imaging, this paper presents an aspheric lens with ring (ALR). The ALR has a controlled diameter of 50 mm, can be machined by 3D-printed technology, and does not need to use complex imaging optical paths to achieve the large DOF function. In a transmitted point-scan imaging system with a 140 GHz light source, the lens can achieve both a resolution of 6 mm and an effective DOF of 66.4 mm for objects greater than 27 mm from the lens surface.

Research on wheat impurity identification method based on terahertz imaging technology

The traditional detection of impurities in wheat has difficulties such as low precision, time-consuming, and cumbersome, therefore, it is important to study the method of rapid and accurate detection of impurities in wheat for correctly assessing the quality grade of wheat. Terahertz (THz) technology has many superior properties such as transient, broadband, low-energy, and penetrating, which can realize rapid and nondestructive detection of wheat quality. In this study, a classification and recognition algorithm AHA-RetinaNet-X for wheat impurity terahertz images based on RetinaNet and Artificial hummingbird algorithm (AHA) is proposed.A THz three-dimensional tomography imaging system is used to image wheat and its impurities, and two THz image datasets, respectively the wheat and impurity dataset for verifying the classification effect of wheat and impurities and the impurity dataset for verifying the classification effect of impurities. The experimental results show that the AHA-RetinaNet-X model outperforms other detection and classification models in terms of accuracy, F1-score, precision, recall, and specificity, and is able to achieve 96.1%, 94.9%, 95.2%, 95.8%, 95.5%, 95.3%, and 93.3% for the wheat and impurity dataset and the impurity dataset, respectively, 95.6%, 96.3%, and 95.2%, and the mAP value of AHA-RetinaNet-X is also higher than the other models and can reach 92.1%. The combination of THz imaging technology and AHA-RetinaNet-X can realize the classification and identification of wheat and impurities, which provides a new method for the non-contact rapid nondestructive detection and identification of wheat and impurities, and also provides a reference for the research of the identification and detection methods of other substances.

Research on Non-Destructive Quality Detection of Sunflower Seeds Based on Terahertz Imaging Technology.

The variety and content of high-quality proteins in sunflower seeds are higher than those in other cereals. However, sunflower seeds can suffer from abnormalities, such as breakage and deformity, during planting and harvesting, which hinder the development of the sunflower seed industry. Traditional methods such as manual sensory and machine sorting are highly subjective and cannot detect the internal characteristics of sunflower seeds. The development of spectral imaging technology has facilitated the application of terahertz waves in the quality inspection of sunflower seeds, owing to its advantages of non-destructive penetration and fast imaging. This paper proposes a novel terahertz image classification model, MobileViT-E, which is trained and validated on a self-constructed dataset of sunflower seeds. The results show that the overall recognition accuracy of the proposed model can reach 96.30%, which is 4.85%, 3%, 7.84% and 1.86% higher than those of the ResNet-50, EfficientNeT, MobileOne and MobileViT models, respectively. At the same time, the performance indices such as the recognition accuracy, the recall and the F1-score values are also effectively improved. Therefore, the MobileViT-E model proposed in this study can improve the classification and identification of normal, damaged and deformed sunflower seeds, and provide technical support for the non-destructive detection of sunflower seed quality.

Root phenotype detection of rice seedling under nitrogen conditions based on terahertz imaging technique

Root phenotype detection is the basis for screening of dominant root and improving of seed breeding. The efficiency of root phenotype detection is restricted by the concealment and complexity of crop roots. In this paper, a new method based on terahertz imaging technology was proposed to detect the phenotypic characteristics of rice root and quantitatively predict root nitrogen content. First, the terahertz imaging spectral data of rice root were preprocessed with de-overlapping, reconstruction, enhancement, and segmentation. Then, the phenotypic characteristics of root length, diameter and surface area of rice roots were extracted according to the refinement algorithm. In addition, three linear regression models were fitted between root phenotype and root nitrogen content. Finally, Convolutional Neural Network (CNN), Genetic Algorithm-Back Propagation Neural Network (GA-BPNN), and Sparrow Search Algorithm-Support Vector Regression (SSA-SVR) were used to predict nitrogen content in rice roots by training and testing terahertz time domain data. Compared with two kinds of root phenotypic analysis software, the average errors of root length, diameter and root surface area calculated in this paper were 10.68 %, 7.29 % and 3.49 %, respectively. The fitting determination coefficients between root length, root surface area and the root nitrogen content were 0.88 and 0.87 after removing three groups of high-nitrogen data and one group of contrast data. The prediction accuracy of SSA-SVR model was 0.99 and the root mean square error was 0.05. Studies show that terahertz imaging technique can provide an effective analytical way for qualitative analysis of root phenotype.

SMR–YOLO: Multi-Scale Detection of Concealed Suspicious Objects in Terahertz Images

The detection of concealed suspicious objects in public places is a critical issue and a popular research topic. Terahertz (THz) imaging technology, as an emerging detection method, can penetrate materials without emitting ionizing radiation, providing a new approach to detecting concealed suspicious objects. This study focuses on the detection of concealed suspicious objects wrapped in different materials such as polyethylene and kraft paper, including items like scissors, pistols, and blades, using THz imaging technology. To address issues such as the lack of texture details in THz images and the contour similarity of different objects, which can lead to missed detections and false alarms, we propose a THz concealed suspicious object detection model based on SMR–YOLO (SPD_Mobile + RFB + YOLO). This model, based on the MobileNext network, introduces the spatial-to-depth convolution (SPD-Conv) module to replace the backbone network, reducing computational and parameter load. The inclusion of the receptive field block (RFB) module, which uses a multi-branch structure of dilated convolutions, enhances the network’s depth features. Using the EIOU loss function to assess the accuracy of predicted box localization further optimizes convergence speed and localization accuracy. Experimental results show that the improved model achieved mAP@0.5 and mAP@0.5:0.95 scores of 98.9% and 89.4%, respectively, representing improvements of 0.2% and 1.8% over the baseline model. Additionally, the detection speed reached 108.7 FPS, an improvement of 23.2 FPS over the baseline model. The model effectively identifies concealed suspicious objects within packages, offering a novel approach for detection in public places.

Anomalous attenuation phenomenon of terahertz wave through the micron air gap defects in XLPE insulation material

During the fabrication process of the factory joint in submarine cables, manual cutting and polishing are required, which can easily result in scratches, pits, and other defects. Meanwhile, due to the limitations of insulation recovery and injection molding technology, air gap defects are prone to occur inside the joints, posing significant safety hazards. In order to preemptively detect such defects before the cable is put into operation, this study mimics the insulation recovery process of the cable joint to create micron air gaps inside the insulation and uses terahertz imaging technology to visualize them. The results indicate that 153 μm and 76 μm arc-shaped air gaps can be clearly recorded by terahertz imaging systems. Meanwhile, terahertz time-domain signals clearly show that the electromagnetic waves experience a significant attenuation of up to 45 % after passing through the arc-shaped air gap, and their energy exhibits an uneven distribution. To explain the propagation phenomena of terahertz waves in micron air gaps, this study proposes a propagation model based on absorption effects, reflection effects, partial interference effects, and off-axis Gaussian scattering effects. This paper proved the potential of terahertz waves in detecting air gap defects in cables joint. The associated theoretical models can be utilized to develop targeted imaging algorithms.

Detection the internal quality of watermelon seeds based on terahertz imaging technology combined with image smoothing and enhancement algorithm

AbstractThe cultivation processes of watermelon seed are often affected by issues such as empty shells and defects, resulting in significant losses. To obtain high‐quality seeds, the terahertz imaging technology combined with image smoothing and enhancement algorithm was proposed to reduce the noise and non‐obvious features caused by the influence in the imaging process and realize the non‐destructive, efficient, and accurate detection of the internal quality of watermelon seeds. Initially, a terahertz imaging system with a spatial resolution of 0.4 mm was used to acquire images of watermelon seeds with varying levels of fullness. Subsequently, denoising techniques, including Gaussian filtering, median filtering, bilateral filtering, discrete wavelet transformation denoising, wavelet denoising, and principal component analysis denoising, were used to handle the terahertz spectral images of watermelon seeds in the frequency range of 1–1.5 THz, respectively. Image enhancement operations, involving segmented linear gray‐level transformation and fractional‐order differentiation, were performed on the terahertz images of watermelon seeds after denoising. The optimal image processing approach was determined based on defect assessment through threshold segmentation. Finally, the validation was conducted at a spatial resolution of 0.2 mm. The images at a spatial resolution of 0.4 mm were subjected to wavelet denoising and window slicing in segmented linear gray‐level transformation (WS‐SLT) enhancement; the results exhibited the following improvements in defect accuracy compared with untreated THz images. A 7.74% increase in accuracy was observed for empty seeds, along with a 6.29% increase in the defect ratio for defective seeds 1. The defect ratio for intact seeds was 0, and there was no significant difference in defect ratio accuracy for defective seeds 2. At a spatial resolution of 0.2 mm, the average defect ratio error of THz imaging handled by wavelet denoising and WS‐SLT was approximately 5.04%. In conclusion, the terahertz imaging technology coupled with wavelet denoising and WS‐SLT methods can be used to enhance the accuracy of internal defect detection in watermelon seeds, and it provides a technical foundation and reference for assessing watermelon seed fullness.

Imaging of biological tissues with terahertz microscopy based on a cross-filament

Terahertz (THz) imaging technology has shown powerful potential for applications in biological sensing due to characteristics of the THz radiation. However, the measurement accuracy is always restricted by the low spatial resolution of THz imaging. In this work, a THz microscopy based on a cross-filament was utilized to inspect different biological tissues. A quasi sub-wavelength resolution was realized without approaching the sampe surface with any actual devices. By using the technique, fine tissue structures of two plant leaves and two animal bodies were precisely identified and analyzed from their temporal, spectral, and absorption images. Furthermore, possible optimization solutions were also discussed in detail for enhancing the imaging performance. This work demonstrated a novel and practical inspection method for pushing the development of THz biological applications.

Enhanced terahertz emission in a large-area photoconductive antenna through an array of tightly packed sapphire fibers

Experimental observation of the enhanced terahertz (THz) emission in a large-area photoconductive antenna-emitter (LAE), boosted by an array of cylindrical sapphire-fiber-based microlenses, is reported. The observed enhancement is achieved, thanks to the sharp focusing of a pump laser beam near the semiconductor surface, for which the high-refractive-index sapphire lenses are used. We predict numerically and confirm experimentally a considerable enhancement in the emitted THz spectral power for such a sapphire-fiber-coupled LAE, as compared to an ordinary one with an equal electrode topology. In fact, a ≃8.5-fold THz power boost is achieved, resulting in a +9.3 dB increase in the dynamic range. The results of our findings can be used to improve the performance of large-area THz devices, aimed at meeting the demands of rapidly developed THz spectroscopy, imaging, sensing, and exposure technologies.

Lightweight YOLOv7 Algorithm for Multi-Object Recognition on Contrabands in Terahertz Images

With the strengthening of worldwide counter-terrorism initiatives, it is increasingly important to detect contrabands such as controlled knives and flammable materials hidden in clothes and bags. Terahertz (THz) imaging technology is widely used in the field of contraband detection due to its advantages of high imaging speed and strong penetration. However, the terahertz images are of poor qualities and lack texture details. Traditional target detection methods suffer from low detection speeds, misdetection, and omission of contraband. This work pre-processes the original dataset using a variety of image processing methods and validates the effect of these methods on the detection results of YOLOv7. Meanwhile, the lightweight and multi-object detection YOLOv7 (LWMD-YOLOv7) algorithm is proposed. Firstly, to meet the demand of real-time for multi-target detection, we propose the space-to-depth mobile (SPD_Mobile) network as the lightweight feature extraction network. Secondly, the selective attention module large selective kernel (LSK) network is integrated into the output of the multi-scale feature map of the LWMD-YOLOv7 network, which enhances the effect of feature fusion and strengthens the network’s attention to salient features. Finally, Distance Intersection over Union (DIOU) is used as the loss function to accelerate the convergence of the model and to have a better localisation effect for small targets. The experimental results show that the YOLOv7 algorithm achieves the best detection results on the terahertz image dataset after the non-local mean filtering process. The LWMD-YOLOv7 algorithm achieves a detection accuracy P of 98.5%, a recall R of 97.5%, and a detection speed of 112.4 FPS, which is 26.9 FPS higher than that of the YOLOv7 base network. The LWMD-YOLOv7 achieves a better balance between detection accuracy and detection speed. It provides a technological reference for the automated detection of contraband in terahertz images.

Biomedical application of terahertz imaging technology: a narrative review.

Terahertz (THz) imaging has wide applications in biomedical research due to its properties, such as non-ionizing, non-invasive and distinctive spectral fingerprints. Over the past 6 years, the application of THz imaging in tumor tissue has made encouraging progress. However, due to the strong absorption of THz by water, the large size, high cost, and low sensitivity of THz devices, it is still difficult to be widely used in clinical practice. This paper provides ideas for researchers and promotes the development of THz imaging in clinical research. The literature search was conducted in the Web of Science and PubMed databases using the keywords "Terahertz imaging", "Breast", "Brain", "Skin" and "Cancer". A total of 94 English language articles from 1 January, 2017 to 30 December, 2022 were reviewed. In this review, we briefly introduced the recent advances in THz near-field imaging, single-pixel imaging and real-time imaging, the applications of THz imaging for detecting breast, brain and skin tissues in the last 6 years were reviewed, and the advantages and existing challenges were identified. It is necessary to combine machine learning and metamaterials to develop real-time THz devices with small size, low cost and high sensitivity that can be widely used in clinical practice. More powerful THz detectors can be developed by combining graphene, designing structures and other methods to improve the sensitivity of the devices and obtain more accurate information. Establishing a THz database is one of the important methods to improve the repeatability and accuracy of imaging results. THz technology is an effective method for tumor imaging. We believe that with the joint efforts of researchers and clinicians, accurate, real-time, and safe THz imaging will be widely applied in clinical practice in the future.

Terahertz imaging technology for localization of cancer tumours: a technical review

Currently, cancer is a large contributing factor in the increased mortality rates and at present the predictions are estimating an increased trend. The conventional medical cancer imaging modalities, for example X-Ray and Computed Tomography use ionizing radiation which is not tissue friendly for repeated assessments. The Terahertz (THz) cancer imaging offers novel opportunities for non-ionizing, non-invasive and early cancer detection, or diagnosis as well as improved cancer patient treatment follow-ups. In this review, a broad overview is given on the potential of THz radiation-based imaging and sensing as a technique for detection of various cancers cells. The THz radiation dynamics and interaction mechanisms with biological systems as well as parameter extraction and modelling for the observed THz image contrast are studied. The experimental studies on THz imaging and sensing are investigated with the goal approach to investigate the ex vivo, in vitro, and in vivo observations. The use of advanced analytic algorithms, specifically deep learning, is proposed for improved detection, discrimination of complex tissue with overlapping dielectric properties and development of clinical decision support systems. Research gaps in the THz imaging studies are identified based on recent trends, latest strategies suggested and the roadmap for future research direction provided.

Vanadium dioxide thin films-assisted terahertz meta-surface for simultaneous absorption, polarization conversion bi-functional switching, and wavefront operation

Actively switchable metasurfaces are extremely useful for achieving a multifunctional integrated platform. The proposed design enables functional switching through the temperature-induced phase transition features of vanadium dioxide (VO2). The hypothesized metasurface can be considered a broadband absorber when VO2 is in the metallic phase, with an absorption rate approaching 90% in the frequency spectrum of 1.41–2.69 THz, according to numerical simulations. When VO2 is in the insulating phase, the proposed metasurface acts as a half-wave plate with more than 80% polarization conversion rate (PCR) in the frequency range of 0.71–2.72 THz. Furthermore, the dynamic modulation of absorbance and PCR can be achieved when the VO2 conductivity varies within a specific range. Finally, by changing the opening angle of the gold split-ring resonator to introduce an abrupt phase at the interface, the composed metacell can perform arbitrary manipulation of reflected beams in the 1.0–2.2 THz broadband range; we also design 1D and 2D focusing metalenses. Both have good subwavelength focusing characteristics. Therefore, the proposed metasurface can be effectively applied in sixth-generation communication systems, terahertz imaging, and other technologies.

Online thickness measurements of acrylate-based coatings on knitted polyester fabric using terahertz time-domain spectroscopy

Three different institutions combined their expertise in a joint R&D project to demonstrate the suitability of terahertz time-domain spectroscopy for thickness measurements of acrylate-based coatings on polyester fabric. First, we spectroscopically analyzed the materials involved using a high-precision terahertz spectrometer and determined optical properties such as the refractive index and loss coefficient. Raster scans were then performed across the sample, and terahertz images were created to identify material boundaries and thickness inhomogeneities. Finally, we applied a fast scanning terahertz time-domain spectrometer based on electronically controlled optical sampling and a customized reflection head for online thickness measurements in a coating line. The textile moved at a speed of 0.5 m/min, while the measurement system records terahertz pulses at a rate of 1600 traces/s. Coating thicknesses of between 63 µm and 99 µm were calculated in a post-processing step. To classify these non-contact and non-destructive measurements, these thicknesses were compared with the results of standard methods. Online and offline (62-83 µm) terahertz and SEM (66-75 µm) measurements delivered the best agreement for average coating thickness. In contrast, mechanical testing methods gave either too low or too high values for layer thickness, which were not comparable. Terahertz time-domain spectroscopy and imaging technology enable the first non-destructive quality control of acrylate-based textile coatings immediately following coating in the production line. Measurable variables are coating thicknesses and the detection of defects such as air inclusions.

Research on internal defect detection of epoxy glass fiber composites based on terahertz imaging technology

Composite materials are characterized by light weight and high specific strength, which have been widely used in the field of transportation. The rapid and non-destructive detection of defects in the process of composite material production can be regarded as the key and guarantee for the safety. In this paper, the terahertz imaging technology was used to explore the detection of epoxy glass fiber composite samples with varied defects. Terahertz time-domain spectroscopy (THz-TDS) of different depths of defects in epoxy glass fiber samples were corrected by multiple pretreatment methods. Different variable selection methods were used to screen the spectral data. The Partial Least Squares (PLS) and Least Squares Support Vector Machines (LS-SVM) defect depth quantitative models were established, respectively. The visualization of the defect area is achieved by combining with different types of binary threshold segmentation methods. It was found that the ALS-SPA-LS-SVM model presents the best effects, and Rp and RMSEP of prediction set are 0.9998 and 0.0152 respectively. The grayscale histogram bimodal method has the best effect on detecting the defect area. It is feasible to detect the defects in epoxy glass fiber by THz-TDS combined with LS-SVM and image processing technology, which can achieve rapid and nondestructive detection of the location, depth and area of the defects. It provides the technical basis for defect detection in the process of composite material production. In this way, this study provides new methods and means for defect detection and quality supervision of other types of composite materials.

Research on Rice Seed Fullness Detection Method Based on Terahertz Imaging Technology and Feature Extraction Method

Research on Rice Seed Fullness Detection Method Based on Terahertz Imaging Technology and Feature Extraction Method

Enhance Terahertz Image Resolution via Complex-valued convolutional neural networks

Terahertz imaging technology is widely used in nondestructive testing (NDT), human security inspection, far-field imaging systems, and so on. Improving image resolution is always a topic of discussion. For visible image and image super-resolution, the convolutional neural network (CNN) has achieved a better improvement effect than the traditional algorithm. Compared to optics, it is quite feasible to gain both the amplitude and phase information in THz imaging, the Complex-valued convolutional neural network (CV-CNN) is better suited for the field of terahertz imaging than the real-valued neural network. In this paper, A lightweight complex-valued neural network is constructed which enhances terahertz imaging in a vector network analyzer (VNA) imaging system. Compared to CNN, CV-CNN has a better peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), and it is significantly less vulnerable to overfitting. Phase information can be used well at the same time, which is impossible for CNN. The network is trained using the MNIST dataset and verified by using simulated and measured data obtained from a 200Ghz imager.

Terahertz compressed sensing imaging based on line array detection

In this paper, a terahertz compressed sensing imaging technique based on line array detection is proposed. The feasibility of the proposed terahertz compressed sensing imaging based on line array detection is verified by theoretical derivation, MATLAB and ZEMAX simulation as well as practical experiment. Firstly, we compare the traditional single-pixel compressed sensing imaging, block compressed sensing imaging and the proposed terahertz compressed sensing imaging based on line array detection by MATLAB simulation, and the results show that the proposed technique can achieve high-quality image reconstruction. Then, we import self-designed mask and lenses as well as other devices into ZEMAX to simulate the compressive sensing process of line array detection using non-sequential mode. According to the simulation results, a terahertz line array compressive sensing imaging system is built using a high-speed line-array detector. The experimental results show that the technique has higher imaging stability and the sampling process is highly generalizable to different objects. Compared with the traditional single-pixel random sampling compressed sensing method, this method greatly reduces the sampling rate and successfully reconstructs the original image at a sampling rate of 16%. We believe that the compressed sensing terahertz imaging technology based on line array detection proposed in this paper provides a new idea for terahertz multi-pixel compressed sensing imaging, which is expected to promote the development of terahertz high-resolution rapid imaging. In addition, this technology can be applied to both terahertz line and area scan imaging systems, which has great application potential in correcting image distortion caused by movement of imaging objects.