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A three-stage detection algorithm for automatic crack-width identification of fine concrete cracks

JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING 2024年第6期14卷 1373-1382页

作者： Huang, Huang Wu, Zhishen Shen, Haifeng Ibaraki Univ Dept Urban & Civil Engn Hitachi 3168511 Japan Southeast Univ Int Inst Urban Syst Engn Nanjing Peoples R China

Semantic image segmentation is extensively used for automatic concrete crack detection. In previous studies on semantic image segmentation, concrete images were usually labeled as crack and noncrack zones, and recognition models were then trained using artificial neural networks. However, there is not enough edge information in concrete images for the trained model to identify effectively fine concrete cracks (widths < 0.1 mm). Furthermore, complex backgrounds in concrete images can cause false detections. To improve efficiency and reduce false detections, this study develops a three-stage automatic crack-width identification method for fine concrete cracks. First, a full crack skeleton information identification based on image segmentation is proposed. The performance of the mainstream image segmentation architectures, PSP-Net, Seg-Net, U-Net, and Res-Unet, are compared and analyzed, demonstrating that the Res-Unet-based crack skeleton segmentation is the most accurate at fine-crack detection and able to solve the information loss problem that occurs when learning the imbalanced data of fine concrete cracks. Second, a fractal dimension (FD)-based false detection removal process is applied to discriminate true cracks and false detections. The results show that false detections (line-like curves, shadows, and surface stains) can be removed, increasing the matching rate from 0.6476 to 0.8351. Finally, the FD features of the crack skeleton with maximum widths < 0.1 mm, crack widths in the range of 0.1-0.2 mm, and crack widths > 0.2 mm are calculated. Findings illustrate that the values of the FD feature for the three crack-width ranges are suitable for quantitative characterization of identified crack widths.

关键词： detection algorithm Image segmentation Concrete crack-width identification Artificial neural network

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MechaTag: A Mechanical Fiducial Marker and the detection algorithm

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2021年第3期103卷 1-11页

作者： Digiacomo, Francesca Bologna, Francesco Inglese, Francesco Stefanini, Cesare Milazzo, Mario St Anna Sch Adv Studies Biorobot Inst Viale Rinaldo Piaggio 34 I-56025 Pontedera PI Italy Baker Hughes Co Nuovo Pignone Tecnol Srl Florence Italy

Fiducial markers are fundamental components of many computer vision systems that help, through their unique features (e.g., shape, color), a fast localization of spatial objects in unstructured scenarios. They find applications in many scientific and industrial fields, such as augmented reality, human-robot interaction, and robot navigation. In order to overcome the limitations of traditional paper-printed fiducial markers (i.e. deformability of the paper surface, incompatibility with industrial and harsh environments, complexity of the shape to reproduce directly on the piece), we aim at exploiting existing, or additionally fabricated, structural features on rigid bodies (e.g., holes), developing a fiducial mechanical marker system called MechaTag. Our system, endowed with a dedicated algorithm, is able to minimize recognition errors and to improve repeatability also in case of ill boundary conditions (e.g., partial illumination). We assess MechaTag in a pilot study, achieving a robustness of fiducial marker recognition above 95% in different environment conditions and position configurations. The pilot study was conducted by guiding a robotic platform in different poses in order to experiment with a wide range of working conditions. Our results make MechaTag a reliable fiducial marker system for a wide range of robotic applications in harsh industrial environments without losing accuracy of recognition due to the shape and material.

关键词： Computer vision Fiducial marker Robot navigation detection algorithm

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Convolutional-Neural-Network-Based detection algorithm for Uplink Multiuser Massive MIMO Systems

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IEEE ACCESS 2020年 8卷 64250-64265页

作者： Li, Lin Hou, Huijun Meng, Weixiao Nanjing Univ Posts & Telecommun Jiangsu Key Lab Broadband Wireless Commun & Inter Nanjing 210046 Peoples R China Nanjing Univ Posts & Telecommun Sch Internet Things Nanjing 210046 Peoples R China CETC 14th Res Inst Nanjing 210039 Peoples R China Harbin Inst Technol Commun Res Ctr Harbin 150001 Peoples R China

In the coming 6th generation (6G) and beyond in wireless communication, an increasing number of ultrascale intelligent factors, including mobile robot users and smart cars, will result in interference exploitation. The management of this exploitation will be a great challenge for detection algorithms in uplink massive multiple-input and multiple-output (MIMO) systems, especially for high-order quadrature amplitude modulation (QAM) signals. Artificial intelligence technology employing machine learning is one of the key approaches among the 6G technical solutions. In this paper, a convolutional-neural-network-based likelihood ascent search (CNNLAS) detection algorithm is proposed on the basis of a graphical detection model for uplink multiuser massive MIMO systems. Compared with other algorithms, the proposed CNNLAS detection algorithm has a stronger robustness against the channel estimation errors, and requires lower average received signal-to-noise ratios to obtain better bit error rate performance and to achieve the theoretical spectral efficiency with a lower polynomial average per symbol computational complexity, both for the graphical low-order and high-order QAM signals in uplink multiuser massive MIMO systems.

关键词： detection algorithms Uplink Massive MIMO Transmitting antennas Quadrature amplitude modulation Bit error rate Wireless communication massive multiple-input and multiple-output (MIMO) convolutional neural network (CNN) detection algorithm high-order modulation bit error rate (BER) computational complexity

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Crop-row detection algorithm based on Random Hough Transformation

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MATHEMATICAL AND COMPUTER MODELLING 2011年第3-4期54卷 1016-1020页

作者： Ji, Ronghua Qi, Lijun China Agr Univ Coll Informat & Elect Engn Beijing 100083 Peoples R China

It is important to detect crop rows accurately for field navigation. In order to spray on line, a variable rate spray system should detect the crop center line accurately. Most existing detection algorithms are slow to detect crop rows because of the complicated calculation. The gradient-based Random Hough Transform algorithm could improve the calculation speed and reduce the computation effectively by the more-to-one merger mapping method. In order to detect the center of the crop row rapidly and effectively, the detection algorithm with gradient-based Random Hough Transform was proposed to detect the center line of crop rows. We tested the center line of crop-row detection for three kinds of plant distribution, being sparse, general and intensive. The experimental results showed that the detection algorithm with gradient-based Random Hough Transform was adaptive to the difference of plant density in the crop row effectively. Contrasted with the detection algorithm based on the Hough transform, the detection algorithm based on the gradient-based Random Hough was faster and had a high detection correction rate. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： detection algorithm Random Hough Transform Crop row

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Analysis of Data Point Cloud Preprocessing and Feature Angle detection algorithm

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RECENT ADVANCES IN ELECTRICAL & ELECTRONIC ENGINEERING 2021年第7期14卷 700-707页

作者： Zhao, Feng Dhiman, Gaurav Wuhan Business Univ Modern Educ Technol Ctr Wuhan 430056 Hubei Peoples R China Govt Bikram Coll Commerce Dept Comp Sci Patiala Punjab India

Background: The two main stages are utilized for feature extraction, from which the first stage consists of a penalty weight to the neighbor graph's edges. The edge penalty weights are minimized by the neighbor sub-graph extraction to produce the set of feature patterns. For noisy data, the second stage is helpful. Methodology: In order to realize the measurement of the geometric dimensions of the ship block, this paper uses the theory of computer vision and reverse engineering to obtain the data of the segmented-hull with the method of digitizing the physical parts based on the vision, and processes the data by using the relevant knowledge of reverse engineering. Result: The results show that the efficiency of the edge extraction algorithm based on mathematical morphology is 30% higher than that of the mesh generation method. An adaptive corner detection algorithm based on the edge can adaptively determine the size of the support area and accurately detect the corner position. Conclusion: According to the characteristics of the point cloud of ship hull segment data, an adaptive corner detection algorithm based on the edge is adopted to verify its feasibility.

关键词： Data preprocessing corner detection reverse engineering segmented hull detection algorithm edge extraction

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YOLOX-Dense-CT: a detection algorithm for cherry tomatoes based on YOLOX and DenseNet

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JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2022年第6期16卷 4788-4799页

作者： Zheng, Hao Wang, Guohui Li, Xuchen Xian Technol Univ Sch Optoelect Engn Xian 710021 Peoples R China

The detection of cherry tomatoes has a great significance for robotic harvesting. However, uneven environment conditions, such as branch and leaf occlusion, cluster of fruits, and so on, have made the cherry tomatoes detection very challenging. This paper proposes an effective cherry tomato detection algorithm called YOLOX-Dense-CT to solve these problems. To be specific, the DenseNet network is treated as the basic backbone of the original YOLOX to make the whole network suitable for the cherry tomatoes. Moreover, the convolutional block attention module (CBAM) attention mechanism is applied to make the features from the backbone more fused with the Neck part. As suggested by the experimental results, the proposed YOLOX-Dense-CT model is effective in detecting cherry tomatoes, with the mean average precision (mAP) reaching 94.80%, which is 4.02% higher than the original YOLOX-L model. Meanwhile, the number of parameters is only 34.6 M, which is 19.6 M lower than the YOLOX-L. Furthermore, the YOLOX-Dense-CT is compared to general target detection models and it has the best detection performance. In summary, the proposed method can well meet the requirements of high accuracy detection and provide a strategy for the cherry tomato detection system.

关键词： Cherry tomatoes Robotic harvesting detection algorithm YOLOX DenseNet

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Image cutting in video media technology application based on detection algorithm

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2023年 1-13页

作者： Xiong, Jiahui Hunan Univ Arts & Sci Changde 415000 Hunan Peoples R China

Speech activity detection algorithm belongs to a kind of means to judge the audio section and non audio section. Audio information diagnosis is composed of audio and noise section, and special treatment means are used for different signals. In the real model test, we can understand that RBF core has super stable attributes compared with other models, and has good self-renewal ability. In the audio information discrimination attribute diagnosis, we can understand the upgrading calculation process, and understand the large-scale counting audio information discrimination calculation process. The removal of pictograph is the primary obstacle to the solution scope of visual information. In the real use process, the classified noises are stored in the image removal with different attributes on a large scale, which plays a great role in the gradual diagnosis of image removal such as image upgrade and image classification. Therefore, in order to update the image level, eliminate the noise object and gradually diagnose the image, exploring the image removal method has become the first influential step in the first step of image diagnosis. In the era of interconnection, we began to further explore the merging means model of interconnection, visualization and information interoperability, focusing on the governance of the merging and upgrading means model on the premise of interconnection, visualization and information interoperability, We can also apply multi-channel upgrading to upgrade our own hardware and software strength, observe and learn the quality level of the mode and class, so that the practical role of the means of mutual transmission of interconnected visual information can go on for a longer time.

关键词： Voice activity detection algorithm Image cutting Video media technology

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Mature Tomato Fruit detection algorithm Based on improved HSV and Watershed algorithm

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IFAC-PapersOnLine 2018年第17期51卷 431-436页

作者： Malik, Muhammad Hammad Zhang, Ting Li, Han Zhang, Man Shabbir, Sana Saeed, Ahmed Key Laboratory of Modern Precision Agriculture System Integration Research Ministry of Education China Agricultural University Haidian Beijing China Beijing Beida Software Engineering Co. Ltd. China College of Information and Electrical Engineering China Agricultural University HaidianBeijing China

Tomato is well known fruit since it has many essential and beneficial nutrients like antioxidant, vitamin C and A for human daily diet. Tomato picking by hand is both labor and time consuming. Therefore, to overcome these issues, tomato needs to be picked up automatically with the help of harvesting robot. Recently automation of fruit harvesting gains great popularity. To guide the harvesting robot to pick up the fruit correctly, it is important to correctly detect and find the location of the red mature fruit. In this study, a new mature tomato detection algorithm based on the improved HSV (Hue, Saturation, Value) color space and the improved watershed segmentation was proposed, by using a cheap and easily accessible RGB camera for guiding a robot to pick up red tomatoes automatically. For that purpose, the color images of tomato plants were captured under natural light. At the first stage, improved HSV transform was used to remove background and to detect only red tomatoes. Then the morphological operations were applied to modify the detected fruit. The second step was the separation of connected fruits, so that the harvesting robot can pick up fruit correctly. To achieve this task, the improved watershed algorithm was applied. As a result, the detection of separated red mature tomato was done by using the improved HSV and watershed, with uneven illumination and complex background. The overall red fruit detection accuracy was up to 81.6%, and it shows good potential to be used on harvesting robot. © 2018

关键词： Fruits Color Computer vision Error detection Harvesting Mathematical morphology Pickups Robots Signal detection Watersheds detection accuracy detection algorithm Harvesting robot Localization Morphological operations Uneven illuminations Water shed algorithm Watershed segmentation

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Coherent integration and detection algorithm for hypersonic target based on modified pulse compression and Keystone transform

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ELECTRONICS LETTERS 2021年第25期57卷 989-991页

作者： Yu, Wenchao Lu, Xingyu Su, Weimin Gu, Hong Yang, Jianchao Nanjing Univ Sci & Technol Sch Elect & Opt Engn Nanjing Peoples R China

This letter considers the coherent integration and detection for hypersonic target in the first-order approximate (FOA) echo model. The proposed algorithm first accomplishes the modified pulse compression utilising the designed reference signal, then conducts the Keystone transform (KT) based coherent integration method to detect the target. Compared with the existing methods which model the received signal without considering the target's intrapulse motion, the proposed method has a lower modeling error and can maintain good noise robustness for weak hypersonic target detection. Experimental results are given to show the effectiveness of the proposed method.

关键词： modified pulse compression utilising radar imaging detection algorithm approximation theory first-order approximate coherent integration method target tracking radar tracking lower modeling error object detection designed reference signal Doppler radar weak hypersonic target detection radar detection received signal Keystone transform radar signal processing

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An iPhone application using a novel stool color detection algorithm for biliary atresia screening

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PEDIATRIC SURGERY INTERNATIONAL 2017年第10期33卷 1115-1121页

作者： Hoshino, Eri Hayashi, Kuniyoshi Suzuki, Mitsuyoshi Obatake, Masayuki Urayama, Kevin Y. Nakano, Satoshi Taura, Yasuyuki Nio, Masaki Takahashi, Osamu St Lukes Int Univ Ctr Clin Epidemiol Ctr Clin Acad Chuo Ku 5th FloorTsukiji 3-6-2 Tokyo 1040045 Japan Juntendo Univ Grad Sch Med Dept Pediat Tokyo Japan Kochi Med Sch Dept Pediat Surg Nankoku Kochi Japan Natl Ctr Child Hlth & Dev Dept Social Med Tokyo Japan Nagasaki Univ Sch Biomed Sci Dept Surg Oncol Nagasaki Japan Tohoku Univ Sch Med Dept Pediat Surg Sendai Miyagi Japan

The stool color card has been the primary tool for identifying acholic stools in infants with biliary atresia (BA), in several countries. However, BA stools are not always acholic, as obliteration of the bile duct occurs gradually. This study aims to introduce Baby Poop (Baby unchi in Japanese), a free iPhone application, employing a detection algorithm to capture subtle differences in colors, even with non-acholic BA stools. The application is designed for use by caregivers of infants aged approximately 2 weeks-1 month. Baseline analysis to determine optimal color parameters predicting BA stools was performed using logistic regression (n = 50). Pattern recognition and machine learning processes were performed using 30 BA and 34 non-BA images. Additional 5 BA and 35 non-BA pictures were used to test accuracy. Hue, saturation, and value (HSV) were the preferred parameter for BA stool identification. A sensitivity and specificity were 100% (95% confidence interval 0.48-1.00 and 0.90-1.00, respectively) even among a collection of visually non-acholic, i.e., pigmented BA stools and relatively pale-colored non-BA stools. Results suggest that an iPhone mobile application integrated with a detection algorithm is an effective and convenient modality for early detection of BA, and potentially for other related diseases.

关键词： Biliary atresia Screening Stool color iPhone application detection algorithm

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