Optical Tomography Detection Research Articles

Retraction Note: Simulation of optical image detection based on language activity detection algorithm in piano network teaching system

Retraction Note: Simulation of optical image detection based on language activity detection algorithm in piano network teaching system

Retraction Note: Application of optical imaging detection based on embedded edge computing in the evaluation of forest park tourism resources

Retraction Note: Application of optical imaging detection based on embedded edge computing in the evaluation of forest park tourism resources

Retraction Note: Simulation of 3D digital design of clothing fabrics based on optical imaging detection and image acquisition

Retraction Note: Simulation of 3D digital design of clothing fabrics based on optical imaging detection and image acquisition

Retraction Note: Simulation of optical imaging detection based on virtual reality assisted technology in intelligent assistance system for martial arts actions

Retraction Note: Simulation of optical imaging detection based on virtual reality assisted technology in intelligent assistance system for martial arts actions

Retraction Note: Application of optical imaging detection based on IoT devices in sports dance data monitoring and simulation

Retraction Note: Application of optical imaging detection based on IoT devices in sports dance data monitoring and simulation

Research on Artificial Intelligence Algorithm and Optical Imaging Detection Based on Wireless IoT Devices in the Optimization Process of Strength Training

Research on Artificial Intelligence Algorithm and Optical Imaging Detection Based on Wireless IoT Devices in the Optimization Process of Strength Training

Optical imaging detection of extracellular vesicles of miR-146 modified bone marrow mesenchymal stem cells promoting spinal cord injury repair

Bone Marrow mesenchymal Stem Cells (BMSCs) are considered as an important source of cells for regenerative medicine, In particular, Bone Marrow mesenchymal Stem Cells Exosomes (BMSCs-EXO) have the most significant effect in the treatment of Spinal Cord Injury (SCI), but the mechanism of action is still unknown. This study found that compared with other SCI groups, BMSCs-EXO loaded with miR-146a could significantly improve the functional recovery of the hind limbs of SCI rats. Hematoxylin and eosin (H&E) indicated that the lesion area of spinal cord injury was less, nissl staining indicated that the number of nissl bodies remained more; the mechanism may be through inhibiting the expression of IRAK1 and TRAF6, blocking the activation of NF-κB p65, reducing the expression of TNF-α, IL-1β and IL-6 inflammatory factors and oxidative stress, improving the SCI microenvironment, and promoting the repair of neural function. In general, we found that BMSCs-EXO loaded with miR-146a could reduce the inflammatory response and oxidative stress in SCI by inhibiting the activation of IRAK1/TRAF6/NF-κB p65 signaling pathway, and promote the recovery of neurological function in SCI rats.

Low-Power Perovskite Neuromorphic Synapse with Enhanced Photon Efficiency for Directional Motion Perception.

The advancement of artificial intelligent vision systems heavily relies on the development of fast and accurate optical imaging detection, identification, and tracking. Framed by restricted response speeds and low computational efficiency, traditional optoelectronic information devices are facing challenges in real-time optical imaging tasks and their ability to efficiently process complex visual data. To address the limitations of current optoelectronic information devices, this study introduces a novel photomemristor utilizing halide perovskite thin films. The fabrication process involves adjusting the iodide proportion to enhance the quality of the halide perovskite films and minimize the dark current. The photomemristor exhibits a high external quantum efficiency of over 85%, which leads to a low energy consumption of 0.6 nJ. The spike timing-dependent plasticity characteristics of the device are leveraged to construct a spiking neural network and achieve a 99.1% accuracy rate of directional perception for moving objects. The notable results offer a promising hardware solution for efficient optoneuromorphic and edge computing applications.

Design of a new detachable pedicle screw based on medical optical imaging inspection to improve osteoporosis and enhance vertebral body revision effect

Osteoporosis is a common bone disease that often leads to difficulty in vertebrae revision. Traditional pedicle screws are often complicated to operate and have poor visibility during implantation. A new detachable pedicle screw is needed to improve the revision effect. The aim of this study was to design a new detachable pedicle screw based on medical optical imaging to improve the outcome of vertebral revision in osteoporosis, and to improve operational feasibility and visibility. In this study, the parameters related to the degree of osteoporosis were obtained by optical imaging detection of the osteoporotic vertebral body. Then a new detachable pedicle screw was designed according to the test results to improve the effect of vertebral body revision. By preparing and optimizing the material and structure of the screw, it is ensured that it has sufficient mechanical strength and stability. Finally, the visibility and operability of the improved screw during implantation were verified by medical optical imaging. Compared with traditional screws, the new detachable pedicle screw can improve the vertebral body revision in the case of osteoporosis. The optical imaging test results show that the new screw has good visibility and maneuverability, providing more accurate guidance and positioning for the vertebral body revision operation.

RETRACTED ARTICLE: Application of optical imaging detection based on embedded edge computing in the evaluation of forest park tourism resources

RETRACTED ARTICLE: Application of optical imaging detection based on embedded edge computing in the evaluation of forest park tourism resources

RETRACTED ARTICLE: Application of optical imaging detection based on IoT devices in sports dance data monitoring and simulation

RETRACTED ARTICLE: Application of optical imaging detection based on IoT devices in sports dance data monitoring and simulation

RETRACTED ARTICLE: Simulation of 3D digital design of clothing fabrics based on optical imaging detection and image acquisition

RETRACTED ARTICLE: Simulation of 3D digital design of clothing fabrics based on optical imaging detection and image acquisition

RETRACTED ARTICLE: Simulation of optical imaging detection based on virtual reality assisted technology in intelligent assistance system for martial arts actions

RETRACTED ARTICLE: Simulation of optical imaging detection based on virtual reality assisted technology in intelligent assistance system for martial arts actions

RETRACTED ARTICLE: Simulation of optical image detection based on language activity detection algorithm in piano network teaching system

RETRACTED ARTICLE: Simulation of optical image detection based on language activity detection algorithm in piano network teaching system

Damage identification of railway bridge underwater foundations based on optical images

Diagnosing the health status of the underwater foundation of railway bridges is critical for railway environmental assessment. However, traditional underwater optical image detection technology suffers from image blurring. Thus, this paper develops an underwater foundation damage identification method for railway bridges based on optical images, which exploits the various underwater propagation characteristics of different light source wavelengths. This paper mainly reflects the differential characteristics of underwater basic damage images under different wavelength responses by constructing inter-spectral variance feature functions, local feature functions of images, and global feature functions of images. An optical image enhancement model that integrates different wavelength response features has been proposed to effectively enhance basic underwater optical images. An underwater basic damage area automatic recognition method has been developed that can accurately and efficiently extract the boundary contour of the damaged area. Based on combining field test data, the feasibility and effectiveness of this method were verified through comparative analysis with other methods. The results indicate that the damage identification ability of the method proposed in this paper is improved compared to traditional methods, which can effectively improve the quality of underwater optical images and improve the recognition accuracy of damaged areas. It is a new method of underwater damage identification based on optical image, which can provide more effective technical support for the health diagnosis of underwater railway bridges.

Underwater Small Target Detection Based on YOLOX Combined with MobileViT and Double Coordinate Attention

The underwater imaging environment is complex, and the application of conventional target detection algorithms to the underwater environment has yet to provide satisfactory results. Therefore, underwater optical image target detection remains one of the most challenging tasks involved with neighborhood-based techniques in the field of computer vision. Small underwater targets, dispersion, and sources of distortion (such as sediment and particles) often render neighborhood-based techniques insufficient, as existing target detection algorithms primarily focus on improving detection accuracy and enhancing algorithm complexity and computing power. However, excessive extraction of deep-level features leads to the loss of small targets and decrease in detection accuracy. Moreover, most underwater optical image target detection is performed by underwater unmanned platforms, which have a high demand of algorithm lightweight requirements due to the limited computing power of the underwater unmanned platform with the mobile vision processing platform. In order to meet the lightweight requirements of the underwater unmanned platform without affecting the detection accuracy of the target, we propose an underwater target detection model based on mobile vision transformer (MobileViT) and YOLOX, and we design a new coordinate attention (CA) mechanism named a double CA (DCA) mechanism. This model utilizes MobileViT as the algorithm backbone network, improving the global feature extraction ability of the algorithm and reducing the amount of algorithm parameters. The double CA (DCA) mechanism can improve the extraction of shallow features as well as the detection accuracy, even for difficult targets, using a minimum of parameters. Research validated in the Underwater Robot Professional Contest 2020 (URPC2020) dataset revealed that this method has an average accuracy rate of 72.00%. In addition, YOLOX’s ability to compress the model parameters by 49.6% efficiently achieves a balance between underwater optical image detection accuracy and parameter quantity. Compared with the existing algorithm, the proposed algorithm can carry on the underwater unmanned platform better.

Panoramic annular image restoration algorithm by prediction based on the lens design characteristics.

A panoramic annular lens optical system is important in circumferential detection. However, a panoramic annular image (PAI) has severe tangential and radial distortion. This study proposes a 3σ guideline-based method, which determines the unwrapping center point of the PAI, and a prediction method based on the lens design characteristics according to the intrinsic distortion of the PAL to correct, respectively, the tangential distortion and the radial distortion. The proposed methods can ensure the restoration ability and simplify the dependence on experimental conditions. The experimental results confirm the effectiveness of the proposed method, thus enabling future panoramic optical image detection.

Nonlinear optical imaging by detection with optical parametric amplification (invited paper).

Nonlinear optical imaging is a versatile tool that has been proven to be exceptionally useful in various research fields. However, due to the use of photomultiplier tubes (PMTs), the wide application of nonlinear optical imaging is limited by the incapability of imaging under ambient light. In this paper, we propose and demonstrate a new optical imaging detection method based on optical parametric amplification (OPA). As a nonlinear optical process, OPA intrinsically rejects ambient light photons by coherence gating. Periodical poled lithium niobate (PPLN) crystals are used in this study as the media for OPA. Compared to bulk nonlinear optical crystals, PPLN crystals support the generation of OPA signal with lower pump power. Therefore, this characteristic of PPLN crystals is particularly beneficial when using high-repetition-rate lasers, which facilitate high-speed optical signal detection, such as in spectroscopy and imaging. A PPLN-based OPA system was built to amplify the emitted imaging signal from second harmonic generation (SHG) and coherent anti-Stokes Raman scattering (CARS) microscopy imaging, and the amplified optical signal was strong enough to be detected by a biased photodiode under ordinary room light conditions. With OPA detection, ambient-light-on SHG and CARS imaging becomes possible, and achieves a similar result as PMT detection under strictly dark environments. These results demonstrate that OPA can be used as a substitute for PMTs in nonlinear optical imaging to adapt it to various applications with complex lighting conditions.

Image Detection of Insulator Defects Based on Morphological Processing and Deep Learning

Insulators are an important part of transmission lines; failure may threaten the operation of these transmission lines. For insulator defect detection, an optical image detection method based on deep learning and morphological detection is proposed. First of all, the Faster RCNN is used to locate the insulator and extract its target image from the detection image. In the second place, a segmentation method of insulator image is proposed to remove the background of the target image. In order to simplify insulator defect detection, an insulator shape transformation method is proposed to unify all types of insulator detection. Finally, a mathematical model is established in the binary image to describe the defect of the insulator. Experiments show that our proposed Faster RCNN can accurately detect the insulators in the image. Its AP is as high as 0.9175, and its Recall rate is as high as 0.98, which is higher than the common insulator recognition algorithm. The accuracy of the proposed defect detection method is 0.98, which can accurately locate the defect position of the insulator. In order to prove the efficiency of the proposed method, we compared several common detection methods.

High-Speed Lightweight Ship Detection Algorithm Based on YOLO-V4 for Three-Channels RGB SAR Image

Synthetic Aperture Radar (SAR) has become one of the important technical means of marine monitoring in the field of remote sensing due to its all-day, all-weather advantage. National territorial waters to achieve ship monitoring is conducive to national maritime law enforcement, implementation of maritime traffic control, and maintenance of national maritime security, so ship detection has been a hot spot and focus of research. After the development from traditional detection methods to deep learning combined methods, most of the research always based on the evolving Graphics Processing Unit (GPU) computing power to propose more complex and computationally intensive strategies, while in the process of transplanting optical image detection ignored the low signal-to-noise ratio, low resolution, single-channel and other characteristics brought by the SAR image imaging principle. Constantly pursuing detection accuracy while ignoring the detection speed and the ultimate application of the algorithm, almost all algorithms rely on powerful clustered desktop GPUs, which cannot be implemented on the frontline of marine monitoring to cope with the changing realities. To address these issues, this paper proposes a multi-channel fusion SAR image processing method that makes full use of image information and the network’s ability to extract features; it is also based on the latest You Only Look Once version 4 (YOLO-V4) deep learning framework for modeling architecture and training models. The YOLO-V4-light network was tailored for real-time and implementation, significantly reducing the model size, detection time, number of computational parameters, and memory consumption, and refining the network for three-channel images to compensate for the loss of accuracy due to light-weighting. The test experiments were completed entirely on a portable computer and achieved an Average Precision (AP) of 90.37% on the SAR Ship Detection Dataset (SSDD), simplifying the model while ensuring a lead over most existing methods. The YOLO-V4-lightship detection algorithm proposed in this paper has great practical application in maritime safety monitoring and emergency rescue.