Imaging Method For Detection Research Articles

Comparison of the Efficiency of Hyperspectral and Pulse Amplitude Modulation Imaging Methods in Pre-Symptomatic Virus Detection in Tobacco Plants.

Early detection of pathogens can significantly reduce yield losses and improve the quality of agricultural products. This study compares the efficiency of hyperspectral (HS) imaging and pulse amplitude modulation (PAM) fluorometry to detect pathogens in plants. Reflectance spectra, normalized indices, and fluorescence parameters were studied in healthy and infected areas of leaves. Potato virus X with GFP fluorescent protein was used to assess the spread of infection throughout the plant. The study found that infection increased the reflectance of leaves in certain wavelength ranges. Analysis of the normalized reflectance indices (NRIs) revealed indices that were sensitive and insensitive to infection. NRI700/850 was optimal for virus detection; significant differences were detected on the 4th day after the virus arrived in the leaf. Maximum (Fv/Fm) and effective quantum yields of photosystem II (ΦPSII) and non-photochemical fluorescence quenching (NPQ) were almost unchanged at the early stage of infection. ΦPSII and NPQ in the transition state (a short time after actinic light was switched on) showed high sensitivity to infection. The higher sensitivity of PAM compared to HS imaging may be due to the possibility of assessing the physiological changes earlier than changes in leaf structure.

Automatic Detection and Classification of Spread‐F From Ionosonde at Hainan With Image‐Based Deep Learning Method

AbstractAn intelligent Spread‐F image detection and classification method is presented in this paper based on an ionogram image set using deep learning models. The ionogram images from the Hainan station, spanning from 2002 to 2015, have been manually labeled into five categories, resulting in a unique ionogram image set for supervised learning models. To balance the number of different types, simulated noises were added to these images. Based on 80,000 samples with Spread‐F and 20,000 samples without, numerous experiments have been conducted to train VGG, ResNet, EfficientNet, ViT, MobileNet, and other networks. The results on the test set indicate that these models except VGG have a good ability of exacting features of different types, leading to a high level of accuracy in detecting Spread‐F and a relatively accurate classification of it. The ionogram images in 2016 are then employed as another test set to further examine the performance of the trained models. Both quantitative and qualitative analyses have demonstrated the results obtained by deep learning models are highly consistent with manual identification.

Multiscale fire image detection method based on CNN and Transformer

Multiscale fire image detection method based on CNN and Transformer

Ship Target Detection Method in Synthetic Aperture Radar Images Based on Block Thumbnail Particle Swarm Optimization Clustering

Ship target detection is an important application of synthetic aperture radar (SAR) imaging remote sensing in ocean monitoring and management. However, SAR imaging is a form of coherence imaging, meaning that there is a large amount of speckle noise in each SAR image. This seriously affects the detection of an SAR image ship target when the fuzzy C-means (FCM) clustering method is used, resulting in numerous errors and incomplete detection. It is also associated with a slow detection speed, which easily falls into the local minima. To overcome these issues, a new method based on block thumbnail particle swarm optimization clustering (BTPSOC) was proposed for SAR image ship target detection. The BTPSOC algorithm uses block thumbnails to segment the main pixels, which improves the resistance to noise interference and segmentation accuracy, enhances the ability to process different types of SAR images, and reduces the runtime. When particle swarm optimization (PSO) technology is used to optimize the FCM clustering center, global optimization is achieved, the clustering performance is improved, the risk of falling into the local minima is overcome, and the stability is improved. The SAR images from two datasets containing ship targets were used in verification experiments. The experimental results show that the BTPSOC algorithm can effectively detect the ship target in SAR images and that it maintains good integrity with regard to the detailed information from the target region. At the same time, experiments comparing the deep convolution neural network (CNN) and constant false alarm rate (CFAR) were conducted.

Virtual source total focusing method for crack detection in complex curved structure

Accurate non-destructive testing and evaluation of cracks is important for the quality assurance and life-cycle management of key components in aerospace and energy industries. This paper presents a novel imaging method for cracks detection in complex curved structure , combining virtual source technique, multi-mode total focusing method and phase coherence imaging method. The virtual source on the sample surface is employed as the emitter and receiver for the ultrasonic array imaging. In this way, the calculation complexity of imaging algorithm due to beam path search can be reduced, and the detection sensitivity in the region of interest can be improved. In addition, the phase coherence factor is used to suppress the background noise and imaging artifacts, which significantly improves the quality of ultrasonic array imaging. A complex curved sample with variable thickness and non-developable surface was used to examine the effectiveness of the virtual source total focusing method. The signal-to-noise ratio of the crack defect is increased by more than 25 dB using the novel method compared with that of the conventional total focusing method, and the quantitative evaluation error of defect is less than 2.67%.

High-through cell micronucleus image detection method combining multi-attention mechanism and YOLOv5

Cell micronucleus detection is an significant method used to evaluate damage to human cells by drugs, radiation, toxic substances, etc. Aiming at the problems of small target size, high similarity between classes and small sample data in cell micronucleus image recognition, this paper proposes an improved YOLOv5 cell micronucleus high-through detection algorithm named YOLOv5-CEB. Firstly, the basic network is optimized by replacing loss function, resetting anchor box and performing exploratory experiments on network depth. Then, in order to enhance the feature extraction ability of the network, the Bifpn structure is introduced. After that, the semantic connection of context is enhanced by the mechanism of fusing global and local attention, which makes the network pays more attention to ROI. Finally,a shallow detection layer is added, which improve the accuracy of cell micronucleus recognition by multi-scale detection. Compared with the initial YOLOv5 algorithm, the experimental results show that the average precision, accuracy and recall of the YOLOv5-CEB algorithm are improved by 2%, 2% and 1.3%, respectively.

Nanotip-based CMOS photosensitive architecture for highly sensitive near-infrared imaging detection

A type of nanotip-based photosensitive architecture for near-infrared imaging detection is effectively constructed by tightly coupling a chip of common photosensitive complementary–metal–oxide–semiconductor (CMOS) and a silicon nanotip optical antenna. Benefiting from the stimulated surface-wave nano-accumulating over silicon nanotips, the detectors exhibit a highly sensitive near-infrared detection feature with a stable normalized detectivity (D*) of ∼3.25 × 1010 cm·Hz1/2·W−1 and also the maximum value of ∼4.86 × 1011 cm·Hz1/2·W−1 corresponding to weak near-infrared radiation with a nW-level power. Moreover, a polarization sensitive character of the nanotip-based CMOS photosensitive architecture, enabling imaging grayscale closely related to incident radiation level being locally elevated, is also explored. The proposed antenna-based imaging detection approach exhibits a wider radiation response range as well as higher imaging contrast and definition. It can be expected that the imaging detection method will present a further developing potential according to the nanotip-based polarized imaging detection strategy.

Design and Countermeasures of PowerShell-based Attack Techniques to Bypass Portable Executable Image Detection Methods

Design and Countermeasures of PowerShell-based Attack Techniques to Bypass Portable Executable Image Detection Methods

A rapid detection method of towed array seismic surface wave for leakage passage of dyke-dam

Rapid detection of leakage passage in dykes has become a committed step in flood prevention work. The seismic surface wave method can be given the S-wave velocity information of the near-surface (≤ 20 m), and qualitatively evaluate the transverse position, depth range, and direction of the leakage passage. The low acquisition efficiency of the cone geophone in the conventional point measurement mode does not meet the requirements of rapid detection. A single survey line has a small coverage area and lacks comparison with neighboring survey lines, so it is not possible to obtain the direction of extension of the leakage passage. In this paper, we propose a rapid detection method of the towed array seismic surface wave for leakage passage of a dyke-dam. Forty-eight gravity geophones are arranged in three columns, 16 in each column, to form the array geometry. Surface wave imaging methods are inversion and fast imaging. The main shortcomings of inversion calculation are (1) lacking shallow information, (2) low vertical resolution, and (3) multiple solutions. To overcome the above issues, the imaging calculation of the surface wave dispersion curve is implemented using fast imaging method. The proposed array geometry is used to collect seismic data. The simulated data and measured data show that fast imaging has the following advantages: (1) It can make full use of the dispersion curve of broadband seismic data, does not lose shallow information, and has high imaging accuracy. (2) It uses the dispersion curve directly for imaging, does not lose any details of velocity changes, and has a high vertical resolution. (3) The imaging results are unique. In contrast to the inversion results, the fast imaging results clearly distinguish the location of the leakage passage and highlight the lateral variability of the leakage passage. The results of the model data and measured data demonstrate the efficiency and high resolution of the towed array seismic geometry and the fast imaging method for leakage passage detection.

Detection of Cotton Seed Damage Based on Improved YOLOv5

The quality of cotton seed is of great significance to the production of cotton in the cotton industry. In order to reduce the workload of the manual sorting of cotton seeds and improve the quality of cotton seed sorting, this paper proposed an image-detection method of cotton seed damage based on an improved YOLOv5 algorithm. Images of cotton seeds with different degrees of damage were collected in the same environment. Cotton seeds of three different damage degrees, namely, undamaged, slightly damaged, and seriously damaged, were selected as the research objects. Labeling software was used to mark the images of these cotton seeds and the marked images were input into the improved YOLOv5s detection algorithm for appearance-based damage identification. The algorithm added the lightweight upsampling operator CARAFE to the original YOLOv5s detection algorithm and also improved the loss function. The experimental results showed that the mAP_0.5 value of the improved algorithm reached 99.5% and the recall rate reached 99.3% when the uncoated cotton seeds were detected. When detecting coated cotton seeds, the mAP_0.5 value of the improved algorithm reached 99.2% and the recall rate reached 98.9%. Compared with the traditional appearance-based damage detection approach, the improved YOLOv5s proposed in this paper improved the recognition accuracy and processing speed, and exhibited a better adaptability and generalization ability. Therefore, the proposed method can provide a reference for the appearance detection of crop seeds.

A two-stage semi-supervised object detection method for SAR images with missing labels based on meta pseudo-labels

Recently, deep learning has been applied to analyze satellite images. However, applying fully supervised object detection (FSOD) is impractical because it is challenging to detect and annotate objects that are relatively small in high-resolution satellite images. In addition, most data owned by public institutions, such as military reconnaissance videos, do not contain sufficient object class label information. Therefore, the application of semi-supervised objection detection (SSOD) is more practical. The SSOD performance is often determined by generated pseudo-labels. Therefore, this study proposes a 2-stage SSOD model to generate accurate pseudo-labels. In the first stage, annotations, including the location information of unlabeled data, are detected using a modified faster R-CNN model. In the second stage, pseudo-labels of objects are additionally suggested through the meta pseudo-label method. Finally, reliable pseudo-labels are generated by comparing and combining the pseudo-labels generated in each stage, improving the SSOD model performance. Extensive experiments were conducted using the xView3 dataset provided by the U.S. Defense Innovation Unit (DIU). The proposed method performed approximately 11% better than the FSOD model, which does not learn pseudo-labels for datasets with missing labels according to the evaluation metrics proposed by the DIU covering both object detection and classification performance.

YOLO-CID: Improved YOLOv7 for X-ray Contraband Image Detection

Currently, X-ray inspection systems may produce false detections due to factors such as the varying sizes of contraband images, complex backgrounds, and blurred edges. To address this issue, we propose the YOLO-CID method for contraband image detection. Firstly, we designed the MP-OD module in the backbone network to enhance the model’s ability to extract key information from complex background images. Secondly, at the neck of the network, we designed a simplified version of BiFPN to add cross-scale connection lines in the feature fusion structure, to preserve deeper semantic information and enhance the network’s ability to represent objects in low-contrast or occlusion situations. Finally, we added a new object detection layer to improve the model’s accuracy in detecting small objects in dense environments. Experimental results on the PIDray public dataset show that the average accuracy rate of the YOLO-CID algorithm is 82.7% and the recall rate is 81.2%, which are 4.9% and 3.2% higher than the YOLOv7 algorithm, respectively. At the same time, the mAP on the CLCXray dataset reached 80.2%. Additionally, it can achieve a real-time detection speed of 40 frames per second and 43 frames per second in real scenes. These results demonstrate the effectiveness of the YOLO-CID algorithm in X-ray contraband detection.

An improved CNN-based thyroid nodule screening algorithm in ultrasound images

In recent years, the incidence of thyroid nodules has continuously increased. Ultrasound imaging, a preferred method for the clinical diagnosis of thyroid nodules, has the advantages of low cost, non-invasiveness, and real-time display. However, ultrasound imaging has certain issues, such as low contrast and difficulty in the early screening of nodules, which limited its effectiveness. To address these issues, we proposed a nodule detection method for thyroid ultrasound images that applies deep learning and feature extraction mechanisms. Taking YOLOX-M (Size-Medium) as an example, we made improvements to the original algorithm, making it representative of convolutional structure algorithms. To obtain good shallow features, we introduced Involution to increase the spatial specificity of features and provide rich nodule detail. To improve the early screening accuracy of nodules, deformable convolution was introduced in the backbone network for early detection of nodules with different geometric. To alleviate the effects of complex background information in thyroid ultrasound images on the detection of nodules, we introduced an attention mechanism before the feature fusion module to remove redundant information. The experimental results showed that the mAP (All nodules) and mAP@S (Early nodules) of the improved thyroid nodule detection algorithm reached 75.7% and 67.0%, respectively. Compared to the original YOLOX-M algorithm, the detection accuracy was improved by 3.5% and 1.8%. Finally, we integrated this method into other convolutional structure algorithms, which had different degrees of improvement and performance in nodule screening.

Development of an intelligent controller for sports training system based on FPGA

Abstract Training, sports equipment, and facilities are the main aspects of sports advancement. Countries are investing heavily in the training of athletes, especially in table tennis. Athletes require basic equipment for exercises, but most athletes cannot afford the high cost; hence, the necessity for developing a low-cost automated system has increased. To enhance the quality of the athletes’ training, the proposed research focuses on using the enormous developments in artificial intelligence by developing an automated training system that can maintain the training duration and intensity whenever necessary. In this research, an intelligent controller has been designed to simulate training patterns of table tennis. The intelligent controller will control the system that sends the table tennis balls’ intensity, speed, and duration. The system will detect the hand sign that has been previously assigned to different speeds using an image detection method and will work accordingly by accelerating the speed using pulse width modulation techniques. Simply showing the athletes’ hand sign to the system will trigger the artificial intelligent camera to identify it, sending the tennis ball at the assigned speed. The artificial intelligence of the proposed device showed promising results in detecting hand signs with minimum errors in training sessions and intensity. The image detection accuracy collected from the intelligent controller during training was 90.05%. Furthermore, the proposed system has a minimal material cost and can be easily installed and used.

A novel cloud detection method based on segmentation prior and multiple features for Sentinel-2 images

ABSTRACT Clouds in Sentinel-2 images seriously affect its usage in various fields, such as agricultural production and environmental monitoring. Although cloud masks have been provided in Sentinel-2 products, the stability of cloud detection accuracy may be affected by different types of underlying surfaces. This study presents a novel cloud detection method for Sentinel-2 images based on segmentation prior and multiple features. In the presented method, spectral, texture, and exponential features are extracted to enhance the difference between clouds and underlying surfaces. Meanwhile, segmentation results are regarded as priors to constrain pre-classification results to improve the edge accuracy of cloud detection and to obtain detection results with low false alarm rate and low omission rate. Experiments on the Sentinel-2 image dataset show that the presented method achieves good and stable cloud detection results, and the accuracy of cloud detection for six underlying surface types (impervious areas, water, croplands, bare lands, snow & ice, and forest) are above 0.93. These findings demonstrate that the presented method has the potential to effectively improve the stability of cloud detection accuracy while reducing the requirement for the number of samples.

Small object detection in UAV image based on improved YOLOv5

Less effective information is obtained by the object detection network, due to the small size of the detection object in the entire image, the complex background, and the dense object in unmanned aerial vehicle (UAV) images. In response to the difficulties encountered, a small object detection method in UAV images is proposed as an improved YOLOv5-based algorithm in this paper. First, the space-to-depth(SPD) conv module is introduced into the basic feature extraction network, to improve significant loss of image information during downsampling. Then, various attention mechanisms are added, to intensify the acquisition of regions of interest in UAV images. Finally, the multiscale detection module is improved, to enhance the network's ability to detect small objects in UAV images. By conducting experiments on the VisDrone-DET2019 dataset, the test results of the established model show. The improved algorithm achieved a Mean Average Precision (mAP) of 41.8%, which is 7.8% better than the baseline network. In addition, the detection performance is better than most current mainstream target detection algorithms and is of some practical value.

Emission Onset Time-Adjustable Chemiluminescent Gold Nanoparticles with Ultrastrong Emission for Smartphone-Based Immunoassay of Severe Acute Respiratory Syndrome Coronavirus 2 Antigen.

Recently, our group reported a chemical timer approach to manipulate the onset time of chemiluminescence (CL) emission. However, it is still in the proof-of-concept stage, and its analytical applications have not been explored yet. Nanomaterials have merits of good catalytic effect, large specific surface area, good biocompatibility, and ease of self-assembly, which are ideal for constructing analytical-interfaces for bioassays. Herein, an emission onset time-adjustable chemiluminescent L012-Au/Mn2+ was synthesized for the first time by modifying Mn2+ on the surface of L012-protected gold nanoparticle. By using H2O2 and NaHCO3 as coreactants, L012-Au/Mn2+ could not only generate an ultrastrong and long-time CL emission but also its CL emission onset time could be adjusted by the addition of thiourea, which could effectively eliminate interference from the addition of coreactants, shorten the exposure time, reduce the detection background, and finally achieve high sensitivity CL imaging analysis. On this basis, a label-free CL immunoassay was constructed with a smartphone-based imaging system for high-throughput and sensitive determination of severe acute respiratory syndrome coronavirus 2 nucleocapsid (N) protein. The CL image of the immunoassay with different concentrations of N proteins was captured in one photograph 100 s after the injection of H2O2 with a short exposure time of 0.5 s. The immunoassay showed good linearity over the concentration range of 1 pg/mL to 10 ng/mL with a detection limit of 0.13 pg/mL, which was much lower than the reported CCD imaging detection method. In addition, it showed good selectivity and stability and was successfully applied in serum samples from healthy individuals and COVID-19 rehabilitation patients.

Detection of prohibited and restricted object X-ray images based on Hi color space segmentation.

X-ray imaging plays an important role in security inspection. However, the objects are complex, which makes it difficult to automatically detect prohibited and restricted objects. This study aims to develop and test a detection method based on a new image segmentation scheme to solve the problem of detecting prohibited and restricted objects from pseudo-color X-ray images with complex backgrounds. The internal mechanism of the influence of different color spaces on image segmentation effect is explored, and the color space component Hi is studied. Furthermore, the mechanism of the new Hi component and the influence law of its adjustable coefficient are revealed. Additionally, a detection method based on Hi color space segmentation for pseudo-color X-ray images is proposed. The segmentation and detection methods are then tested on actual X-ray images. The results show that hue has the greatest influence on image segmentation effect of the pseudo-color X-ray images. For different pseudo-color X-ray images with complex backgrounds, applying the proposed new Hi color space segmentation method achieves overall accuracy of 0.974 and 1.0 in detecting the gun and knife, respectively. The new X-ray image detection method based on the Hi color space segmentation proposed in this paper enables to better solve the complex background problem including object overlap and adhesion and thus more effectively meet the requirements of actual security inspection.

Crack detection of continuous casting slab by evolutionary topology backbone search

In recent years, computer vision-based methods have been widely used in steel defect detection. Traditional image detection methods mainly rely on manually extracted features, resulting in poor generalization. Deep learning methods are sensitive to the number of samples, and the network structure design relies heavily on manual experience. To address these problems, a backbone network search algorithm based on evolutionary topology is proposed in this paper for crack detection on continuous casting surfaces. Firstly, a variable-length genetic encoding scheme is designed for industrial defect problems with different data complexity, which can improve the applicability of the algorithm and extend the search space. Secondly, to effectively solve the channel redundancy problem in densely connected CNNs, a random pruning strategy for network connection channels is proposed to reduce the topological space and the complexity of the model. Finally, a computational resource allocation mechanism based on a dynamic surrogate model is devised. The surrogate model predicts the individual performance to ensure that computational resources can be concentrated on individuals with better quality. In addition to the steel crack image dataset, the proposed method also uses the workpiece crack image dataset for a supplementary experiment. Experimental results show that the proposed algorithm can achieve better detection performance with fewer computational resources compared to manually designed deep learning algorithms and classical approaches that use evolutionary algorithms to search network architectures.

Temperature monitoring of vehicle brake drum based on dual light fusion and deep learning

The braking system of a vehicle is the most crucial component for ensuring the safety on the road. Moreover, the extraction of temperature field data from a vehicle's tires plays a crucial role in vehicle condition monitoring. Nevertheless, the conventional infrared thermal imaging detection method necessitates manual recognition of the tire components and marking of each temperature point one by one in the infrared image. To address this issue, a method combination with dual-light fusion and deep learning is proposed to implement automatic monitoring for vehicle brake drum temperature. Firstly, we propose a late-end multimodal fusion algorithm based on the YOLOv5 network to integrate visible light and infrared thermal imaging This algorithm allows for feature extraction from both modalities and performing decision-level image fusion, thereby improving the robustness of automatic vehicle and tire detection. Additionally, we employ a semantic segmentation network to accurately segment the tires, extract the relevant visible pixel information, and obtain the temperature field matrix from the infrared images while eliminating extraneous background details. Subsequently, a three-dimensional model of tire’s temperature field is constructed obtaining the dynamics of tire temperature during the vehicle operation. Through experimentation, we demonstrate the efficacy of our proposed method in achieving all-weather roadside vehicle classification and tire temperature monitoring, which provides technical and theoretical support for the active risk prevention and control in road transportation.