Qualitative and event-specific PCR real-time detection methods for StarLink maize

Barcode detection has many applications and detection methods. Most applications have their own requirements for detection accuracy and speed. This paper has its requirement for speed in the real time system to detection inclination defect of barcode. It predominantly researches on two algorithms and their applications on 1-dimentional barcode scanning. One is location and the other is angle of inclination. The algorithms are particularly useful for real time detection of barcodes in online system with image vision devices.

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Real-Time Detection and Identification of Chlamydophila Species in Veterinary Specimens by Using SYBR Green-Based PCR Assays

Applied and Environmental Microbiology ◽

10.1128/aem.00536-11 ◽

2011 ◽

Vol 77 (18) ◽

pp. 6323-6330 ◽

Cited By ~ 5

Author(s):

Steen Nordentoft ◽

Susanne Kabell ◽

Karl Pedersen

Keyword(s):

Real Time ◽

Cost Effective ◽

Sybr Green ◽

Detection Methods ◽

Intracellular Pathogens ◽

Microscopical Examination ◽

Screening Assay ◽

Content Type ◽

Specific Pcr ◽

Pcr Assays

ABSTRACTInfections caused by members of theChlamydiaceaefamily have long been underestimated due to the requirement of special laboratory facilities for the detection of this group of intracellular pathogens. Furthermore, new studies of this group of intracellular pathogens have revealed that host specificity of different species is not as clear as recently believed. As most members of the genusChlamydophilahave shown to be transmissible from animals to humans, sensitive and fast detection methods are required. In this study, SYBR green-based real-time assays were developed that detect all members ofChlamydiaceaeand differentiate the most prevalent veterinaryChlamydophilaspecies:Cp. psittaci,Cp. abortus,Cp. felis, andCp. caviae. By adding bovine serum albumin to the master mixes, target DNA could be detected directly in crude lysates of enzymatically digested conjunctival or pharyngeal swabs or tissue specimens from heart, liver, and spleen without further purification. The assays were evaluated on veterinary specimens where all samples were screened using a family-specific PCR, and positive samples were further tested using species-specific PCRs.Cp. psittaciwas detected in 47 birds,Cp. feliswas found in 10 cats,Cp. caviaewas found in one guinea pig, andCp. abortuswas detected in one sheep. The screening assay appeared more sensitive than traditional microscopical examination of stained tissue smears. By combining a fast, robust, and cost-effective method for sample preparation with a highly sensitive family-specific PCR, we were able to screen forChlamydiaceaein veterinary specimens and confirm the species in positive samples with additional PCR assays.

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Real-Time Detection for Wheat Head Applying Deep Neural Network

Sensors ◽

10.3390/s21010191 ◽

2020 ◽

Vol 21 (1) ◽

pp. 191

Author(s):

Bo Gong ◽

Daji Ergu ◽

Ying Cai ◽

Bo Ma

Keyword(s):

Neural Network ◽

Real Time ◽

Deep Neural Network ◽

Data Augmentation ◽

Detection Methods ◽

Convolutional Network ◽

Head Detection ◽

Real Time Detection ◽

Low Efficiency ◽

Basic Network

Wheat head detection can estimate various wheat traits, such as density, health, and the presence of wheat head. However, traditional detection methods have a huge array of problems, including low efficiency, strong subjectivity, and poor accuracy. In this paper, a method of wheat-head detection based on a deep neural network is proposed to enhance the speed and accuracy of detection. The YOLOv4 is taken as the basic network. The backbone part in the basic network is enhanced by adding dual spatial pyramid pooling (SPP) networks to improve the ability of feature learning and increase the receptive field of the convolutional network. Multilevel features are obtained by a multipath neck part using a top-down to bottom-up strategy. Finally, YOLOv3′s head structures are used to predict the boxes of wheat heads. For training images, some data augmentation technologies are used. The experimental results demonstrate that the proposed method has a significant advantage in accuracy and speed. The mean average precision of our method is 94.5%, and the detection speed is 71 FPS that can achieve the effect of real-time detection.

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NSD-SSD: A Novel Real-Time Ship Detector Based on Convolutional Neural Network in Surveillance Video

Computational Intelligence and Neuroscience ◽

10.1155/2021/7018035 ◽

2021 ◽

Vol 2021 ◽

pp. 1-16

Author(s):

Jiuwu Sun ◽

Zhijing Xu ◽

Shanshan Liang

Keyword(s):

Real Time ◽

Clustering Algorithm ◽

Detection Efficiency ◽

Rapid Development ◽

Outdoor Environment ◽

Detection Methods ◽

Detection Accuracy ◽

Single Shot ◽

Ship Detection ◽

Real Time Detection

With the rapid development of the marine industry, intelligent ship detection plays a very important role in the marine traffic safety and the port management. Current detection methods mainly focus on synthetic aperture radar (SAR) images, which is of great significance to the field of ship detection. However, these methods sometimes cannot meet the real-time requirement. To solve the problems, a novel ship detection network based on SSD (Single Shot Detector), named NSD-SSD, is proposed in this paper. Nowadays, the surveillance system is widely used in the indoor and outdoor environment, and its combination with deep learning greatly promotes the development of intelligent object detection and recognition. The NSD-SSD uses visual images captured by surveillance cameras to achieve real-time detection and further improves detection performance. First, dilated convolution and multiscale feature fusion are combined to improve the small objects’ performance and detection accuracy. Second, an improved prediction module is introduced to enhance deeper feature extraction ability of the model, and the mean Average Precision (mAP) and recall are significant improved. Finally, the prior boxes are reconstructed by using the K-means clustering algorithm, the Intersection-over-Union (IoU) is higher, and the visual effect is better. The experimental results based on ship images show that the mAP and recall can reach 89.3% and 93.6%, respectively, which outperforms the representative model (Faster R-CNN, SSD, and YOLOv3). Moreover, our model’s FPS is 45, which can meet real-time detection acquirement well. Hence, the proposed method has the better overall performance and achieves higher detection efficiency and better robustness.

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Real-Time Small Drones Detection Based on Pruned YOLOv4

Sensors ◽

10.3390/s21103374 ◽

2021 ◽

Vol 21 (10) ◽

pp. 3374

Author(s):

Hansen Liu ◽

Kuangang Fan ◽

Qinghua Ouyang ◽

Na Li

Keyword(s):

Object Detection ◽

Real Time ◽

Processing Speed ◽

State Of The Art ◽

Detection Methods ◽

Detection Accuracy ◽

Small Object ◽

Art Object ◽

Real Time Detection ◽

Small Object Detection

To address the threat of drones intruding into high-security areas, the real-time detection of drones is urgently required to protect these areas. There are two main difficulties in real-time detection of drones. One of them is that the drones move quickly, which leads to requiring faster detectors. Another problem is that small drones are difficult to detect. In this paper, firstly, we achieve high detection accuracy by evaluating three state-of-the-art object detection methods: RetinaNet, FCOS, YOLOv3 and YOLOv4. Then, to address the first problem, we prune the convolutional channel and shortcut layer of YOLOv4 to develop thinner and shallower models. Furthermore, to improve the accuracy of small drone detection, we implement a special augmentation for small object detection by copying and pasting small drones. Experimental results verify that compared to YOLOv4, our pruned-YOLOv4 model, with 0.8 channel prune rate and 24 layers prune, achieves 90.5% mAP and its processing speed is increased by 60.4%. Additionally, after small object augmentation, the precision and recall of the pruned-YOLOv4 almost increases by 22.8% and 12.7%, respectively. Experiment results verify that our pruned-YOLOv4 is an effective and accurate approach for drone detection.

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Surface Defects Detection of Stamping and Grinding Flat Parts Based on Machine Vision

Sensors ◽

10.3390/s20164531 ◽

2020 ◽

Vol 20 (16) ◽

pp. 4531 ◽

Cited By ~ 1

Author(s):

Hongzhi Tian ◽

Dongxing Wang ◽

Jiangang Lin ◽

Qilin Chen ◽

Zhaocai Liu

Keyword(s):

Machine Vision ◽

Real Time ◽

Light Source ◽

Surface Defects ◽

Detection System ◽

Detection Methods ◽

Source Image ◽

Real Time Detection ◽

Anomaly Region ◽

Illumination Mode

Currently, surface defect detection of stamping grinding flat parts is mainly undertaken through observation by the naked eye. In order to improve the automatic degree of surface defects detection in stamping grinding flat parts, a real-time detection system based on machine vision is designed. Under plane illumination mode, the whole region of the parts is clear and the outline is obvious, but the tiny defects are difficult to find; Under multi-angle illumination mode, the tiny defects of the parts can be highlighted. In view of the above situation, a lighting method combining plane illumination mode with multi-angle illumination mode is designed, and five kinds of defects are automatically detected by different detection methods. Firstly, the parts are located and segmented according to the plane light source image, and the defects are detected according to the gray anomaly. Secondly, according to the surface of the parts reflective characteristics, the influence of the reflection on the image is minimized by adjusting the exposure time of the camera, and the position and direction of the edge line of the gray anomaly region of the multi-angle light source image are used to determine whether the anomaly region is a defect. The experimental results demonstrate that the system has a high detection success rate, which can meet the real-time detection rEquation uirements of a factory.

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3D M-Net: Object-Specific 3D Segmentation Network Based on a Single Projection

Computational Intelligence and Neuroscience ◽

10.1155/2021/5852595 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Xuan Li ◽

Sukai Wang ◽

Xiaodong Niu ◽

Liming Wang ◽

Ping Chen

Keyword(s):

Real Time ◽

Structural Features ◽

Detection Methods ◽

X Rays ◽

3D Segmentation ◽

Industrial Products ◽

Specific Product ◽

Robustness To Noise ◽

Quantitative Results ◽

Real Time Detection

The internal assembly correctness of industrial products directly affects their performance and service life. Industrial products are usually protected by opaque housing, so most internal detection methods are based on X-rays. Since the dense structural features of industrial products, it is challenging to detect the occluded parts only from projections. Limited by the data acquisition and reconstruction speeds, CT-based detection methods do not achieve real-time detection. To solve the above problems, we design an end-to-end single-projection 3D segmentation network. For a specific product, the network adopts a single projection as input to segment product components and output 3D segmentation results. In this study, the feasibility of the network was verified against data containing several typical assembly errors. The qualitative and quantitative results reveal that the segmentation results can meet industrial assembly real-time detection requirements and exhibit high robustness to noise and component occlusion.

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DB-YOLO: A Duplicate Bilateral YOLO Network for Multi-Scale Ship Detection in SAR Images

Sensors ◽

10.3390/s21238146 ◽

2021 ◽

Vol 21 (23) ◽

pp. 8146

Author(s):

Haozhen Zhu ◽

Yao Xie ◽

Huihui Huang ◽

Chen Jing ◽

Yingjiao Rong ◽

...

Keyword(s):

Real Time ◽

Spatial Information ◽

Detection Methods ◽

Small Scale ◽

Sar Images ◽

Ship Detection ◽

Multi Scale ◽

Real Time Detection ◽

Bounding Boxes ◽

Improving Accuracy

With the wide application of convolutional neural networks (CNNs), a variety of ship detection methods based on CNNs in synthetic aperture radar (SAR) images were proposed, but there are still two main challenges: (1) Ship detection requires high real-time performance, and a certain detection speed should be ensured while improving accuracy; (2) The diversity of ships in SAR images requires more powerful multi-scale detectors. To address these issues, a SAR ship detector called Duplicate Bilateral YOLO (DB-YOLO) is proposed in this paper, which is composed of a Feature Extraction Network (FEN), Duplicate Bilateral Feature Pyramid Network (DB-FPN) and Detection Network (DN). Firstly, a single-stage network is used to meet the need of real-time detection, and the cross stage partial (CSP) block is used to reduce the redundant parameters. Secondly, DB-FPN is designed to enhance the fusion of semantic and spatial information. In view of the ships in SAR image are mainly distributed with small-scale targets, the distribution of parameters and computation values between FEN and DB-FPN in different feature layers is redistributed to solve the multi-scale detection. Finally, the bounding boxes and confidence scores are given through the detection head of YOLO. In order to evaluate the effectiveness and robustness of DB-YOLO, comparative experiments with the other six state-of-the-art methods (Faster R-CNN, Cascade R-CNN, Libra R-CNN, FCOS, CenterNet and YOLOv5s) on two SAR ship datasets, i.e., SSDD and HRSID, are performed. The experimental results show that the AP50 of DB-YOLO reaches 97.8% on SSDD and 94.4% on HRSID, respectively. DB-YOLO meets the requirement of real-time detection (48.1 FPS) and is superior to other methods in the experiments.

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