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Automatic detection of asphalt pavement thickness: A method combining GPR images and improved canny algorithm

MEASUREMENT 2022年 196卷 1页

作者： Wang, Lutai Gu, Xingyu Liu, Zhen Wu, Wenxiu Wang, Danyu Southeast Univ Sch Transportat Dept Roadway Engn Nanjing 211189 Peoples R China Southeast Univ Natl Demonstrat Ctr Expt Rd & Traff Engn Educ Nanjing 211189 Peoples R China Highway & Transportat Management Ctr Jinhua 321000 Peoples R China

The traditional drill core sampling method used for pavement thickness detection is increasingly difficult to meet the increasing demand for pavement detection. At the same time, ground penetrating radar (GPR) have shown superiority in pavement non-destructive detection for its fast detection, safety and high efficiency. In this study, the traditional canny algorithm was improved by combining wavelet denoising, intercept method and artificial bee colony algorithm, and the improved canny algorithm was compared with the traditional canny algorithm and connected region detection algorithm by combining simulated images and actual images. The detection results showed that the improved canny algorithm had better performance, and the relative error was about 3.82%, which can realize the fast and intelligent detection of asphalt pavement thickness.

关键词： Ground Penetrating Radar Pavement Thickness Digital Image Processing GprMax Non-destructive Detecting improved canny algorithm

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Visual Localization Method for Fastener-Nut Disassembly and Assembly Robot Based on improved canny and HOG-SED

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APPLIED SCIENCES-BASEL 2025年第3期15卷 1645-1645页

作者： Cao, Xiangang Zuo, Mengzhen Chen, Guoyin Wu, Xudong Wang, Peng Liu, Yizhe Xian Univ Sci & Technol Sch Mech Engn Xian 710054 Peoples R China

Visual positioning accuracy is crucial for ensuring the successful execution of nut disassembly and assembly tasks by a fastener-nut disassembly and assembly robot. However, disturbances such as on-site lighting changes, abnormal surface conditions of nuts, and complex backgrounds formed by ballast in complex railway environments can lead to poor visual positioning accuracy of the fastener nuts, thereby affecting the success rate of the robot's continuous disassembly and assembly operations. Additionally, the existing method of detecting fasteners first and then positioning nuts has poor applicability in the field. A direct positioning algorithm for spiral rail spikes that combines an improved canny algorithm with shape feature similarity determination is proposed in response to these issues. Firstly, CLAHE enhances the image, reducing the impact of varying lighting conditions in outdoor work environments on image details. Then, to address the difficulties in extracting the edges of rail spikes caused by abnormal conditions such as water stains, rust, and oil stains on the nuts themselves, the canny algorithm is improved through three stages, filtering optimization, gradient boosting, and adaptive thresholding, to reduce the impact of edge loss on subsequent rail spike positioning results. Finally, considering the issue of false fitting due to background interference, such as ballast in gradient Hough transformations, the differences in texture and shape features between the rail spike and interference areas are analyzed. The HOG is used to describe the shape features of the area to be screened, and the similarity between the screened area and the standard rail spike template features is compared based on the standard Euclidean distance to determine the rail spike area. Spiral rail spikes are discriminated based on shape features, and the center coordinates of the rail spike are obtained. Experiments were conducted using images collected from the field, and the resu

关键词： fastener-nut disassembly and assembly robot spiral rail spike complex environment improved canny algorithm HOG feature similarity center positioning

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Recognition of Low-Altitude Targets Using improved canny Edge Detection 5

Recognition of Low-Altitude Targets Using Improved Canny Edg...

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5th International Conference on Frontiers Technology of Information and Computer, ICFTIC 2023

作者： Song, Yawei Luo, Yuwen Chang, Chunhe Liu, Jianwei Shi, Min Zhai, Jiyao Air Force Early Warning Academy Wuhan China

ISBN: (纸本)9798350309034

Low-altitude aerial target detection is crucial for military, civilian aviation, and unmanned aerial systems. This study enhances canny's edge detection algorithm to accurately identify drones and helicopters. Initial contours are positioned at canny-detected edges and continuously optimized via a snake active contour model, adjusting local thresholds. The improved canny algorithm effectively extracts target contours, enabling accurate recognition and localization. It is robust and adaptable to different environments, overcoming limitations of the traditional canny method. This research provides an effective framework for integrating active contours with canny edge detection for reliable low-altitude target delineation. Context information and machine learning could further optimize edge detection for complex real-world conditions. Overall, this study delivers a robust edge-detection solution tailored to aerial target tracking and analytics across diverse operating environments. © 2023 IEEE.

关键词： improved canny algorithm low-altitude target recognition

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An Enhanced Machine Learning Technique with a Hybrid Metaheuristic algorithm to Identify the Fish Disease

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WIRELESS PERSONAL COMMUNICATIONS 2023年第3期131卷 1529-1549页

作者： Jhansi, G. Sujatha, K. Dr MGR Educ & Res Inst Elect & Commun Engn Chennai Tamil Nadu India Dr MGR Educ & Res Inst Elect & Elect Engn Chennai Tamil Nadu India

Fish diseases in aquaculture pose a significant threat to nutrient security and the fishing industry's income. However, identifying infectious fish can be challenging due to the lack of necessary infrastructure. To prevent the spread of diseases, it is crucial to identify infectious fish promptly. This paper proposes a machine learning technique based on Artificial Neural Networks (ANN) for fish disease identification and classification. We introduce a novel hybrid algorithm called Black Widow Optimization algorithm with Mayfly Optimization algorithm (BWO-MA) for solving global optimization problems. The proposed approach aims to develop a BWO-MA with an ANN-based diagnostic model for the earlier detection of fish diseases. We compare our proposed method with existing machine learning techniques and demonstrate that the BWO-MA-based ANN architecture achieves better performance. Our comprehensive analysis shows that the proposed method achieves state-of-the-art performance. The combination of Artificial Neural Networks (ANN) with the Black Widow Optimization algorithm and with the Mayfly Optimization algorithm (BWO-MA) achieved a high accuracy rate of 96.439% in detecting and classifying fish diseases.

关键词： Median filtering improved canny algorithm Hybrid (BWO-MA) Artificial neural networks (ANN)

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Enhanced canny algorithm for Image Edge Detection in Print Quality Assessment

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TRAITEMENT DU SIGNAL 2023年第3期40卷 1281-1287页

作者： Tao, Nana Zibo Vocat Inst Intelligent Mfg Zibo 255000 Peoples R China

The growing demand for high-quality print output in the digital printing era underscores the importance of refining detection algorithms essential for print quality assessment systems. This study focuses on the analysis and optimization of the classical image edge detection algorithm, the canny algorithm. A novel method is presented, which incorporates an improved adaptive median filter (AMF) for the initial processing of images, resulting in increased efficiency and better handling of noise points. Furthermore, the gradient calculation direction has been expanded, and the threshold has been fine-tuned using an enhanced OTSU algorithm. The optimal threshold selection relies on a preliminary judgement, leading to more comprehensive and accurate image edge information capture. Comparative analysis with the Sobel operator and the traditional canny edge detection highlights the advantages of the optimized canny algorithm. This improved approach succeeds in preserving a greater amount of graphical edge information and exhibits a superior ability to identify false edges, significantly increasing detection accuracy. The findings of this study contribute to the development of print quality detection, promoting a more automated, digital, and systematic approach.

关键词： print quality inspection improved canny algorithm adaptive median filter OTSU algorithm

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Research on Edge Detection algorithm of Railroad Fastener Image based on FPGA FPGA implementation of an efficient railroad fastener image edge detection algorithm 24

Research on Edge Detection Algorithm of Railroad Fastener Im...

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International Conference on algorithms, Software Engineering, and Network Security (ASENS)

作者： Ning, Keqing Chu, Chongchong Xing, Na Du, Minghua North China Univ Technol Beijing 100144 Peoples R China Chinese Peoples Liberat Army Gen Hosp Med Ctr 1 Dept Emergency Beijing 100853 Peoples R China

ISBN: (纸本)9798400709784

Most of the railroad fastener images have the problems of low contrast, complex image noise and difficult to recognize edge *** these difficult problems, this paper improves the traditional canny algorithm by using a hybrid filter composed of adaptive median filter and Gaussian filter to preprocess the image;improves the Sobel operator by using 5*5 eight-direction templates instead of the traditional 3*3 four-direction templates, and expands the computational direction of the non-maximum value suppression module to eight *** experimental results show that the improved algorithm has better image edge detection performance on FPGA, lower resource utilization and faster image processing.

关键词： Railroad fastener images FPGA improved canny algorithm Preprocessing module Nonmaximum value suppression

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Research on Online Identification Method of Foreign Object Invasion in Transmission Lines Based on Image Monitoring

Research on Online Identification Method of Foreign Object I...

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2025 International Conference on Electrical Automation and Artificial Intelligence, ICEAAI 2025

作者： Chen, Cheng Zhang, Han Chen, Xianman Lin, Peng Yuan, Shipeng Wen, Jianpeng Chaozhou Power Supply Bureau of Guangdong Power Grid Co. Ltd. Chaozhou China

ISBN: (纸本)9798331506797

This paper proposes a wire foreign object alarm method for online monitoring of transmission lines to address the issues of low accuracy in monitoring floating objects invading transmission lines and insufficient effectiveness of alarms. This method first constructs an intelligent detection model for floating objects based on a multi-scale RPN network, which realizes the recognition of small-sized hidden danger targets in monitoring scenarios. Subsequently, the article used the improved canny algorithm to extract wires from the images and constructed an effective area for wire foreign object alarm, which was used to remove invalid floating object recognition results such as the ground. Finally, this article conducted tests on the dataset, and the results showed that the proposed method achieved a 97.7% recall rate for wire foreign object alarms, and could greatly reduce false alarms, meeting the needs of intelligent monitoring of transmission lines. © 2025 IEEE.

关键词： Floating objects improved canny algorithm Object detection Transmission line monitoring

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UGVs for Agri Spray with AI assisted Paddy Crop disease Identification

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Procedia Computer Science 2023年 230卷 70-81页

作者： K. Sujatha T. Kalpalatha Reddy N.P.G. Bhavani R.S. Ponmagal V. Srividhya N. Janaki Professor EEE/ECE Department Dr. MGR Educational and Research Institute Chennai-600095 India Asso.Professor ECE Department Saveetha Institute of Medical Sciences Chennai India Professor CSE Department SRM Institute of Science and Technology Kattankolathur India Asso.Professor EEE Department Meenakshi College of Engineering Chennai India Asst. Prof Vels Institute of Science Technology and Advanced Studies Chennai India

This research work involves data access and control of Unmanned Ground Vehicles (UGVs) which is designed for health monitoring of paddy crops as an exception from traditional monitoring system. The proposed scheme of autonomous navigation of drones is planned to be carried out as per the six milestones for disease detection and control in paddy crops. Drones serve as Unmanned Robotic Vehicles (URV) capable of performing desired tasks in unstructured, uncertain and potentially hostile environments and are remotely-operated without human intervention. URVs function completely as autonomous entities in diversified environments. Current UGVs adhere to different levels of automaticity. Typically the vehicle follows high level waypoints spaced for few hundred meters of distance to provide monitoring of agricultural fields and early detection of the various diseases that may occur in the paddy fields in a polyhouse. To increase the vehicle's abilities, tracking efficiency, obstacle avoidance, path planning or lead and follow up augmented control with Fuzzy Logic Controller (FLC) is incorporated. The distributed autonomous system for information gathering related to the paddy crops in polyhouse is enabled using different sensors, which is a data-intensive task. To increase the robustness of the system, fuzzy controllers are proposed to control the navigation of the proposed UGV in “All terrain conditions”. They are needed to offer problem specific heuristic control knowledge for the Inference Engine Design which occurs due to imprecision and uncertainty of the sensor readings. It also requires low computation time which favours the polyhouse situations. The navigation of UGV and the FLC action will in turn depend on “All terrain traversability” and “dead zone” monitoring. The proposed UGV model is capable of measuring the parameters associated with the paddy crops inside a polyhouse. The various diseases in paddy crops are False Smut (FS), Sheath Blight (SB), Rice Blast (RB

关键词： Median filtering improved canny algorithm Convolutional Neural Networks (CNN) Paddy Crop Disease Identification Unmanned Ground Vehicle

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Modulation Recognition algorithm of Radar Signal Based on Icanny-CNN

Modulation Recognition Algorithm of Radar Signal Based on IC...

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International Symposium on Antennas and Propagation (ISAP)

作者： Mao, Xinrui Gao, Jingpeng Qi, Junwei Harbin Engn Univ Coll Informat & Commun Engn Harbin Peoples R China

ISBN: (纸本)9781665431132

Aiming at the low recognition rate on the composite signal modulation in low Signal to Noise Ratio (SNR), this paper proposes a modulation recognition algorithm based on Icanny-CNN. The simulation results show that the proposed algorithm can reach 90.83% at 0dB and 71.52% at -8dB. Therefore, the proposed algorithm has a good classification and anti-noise performance in radar signal modulation recognition and other fields.

关键词： Modulation Recognition Image Processing Composite Radar Signal improved canny algorithm

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A Two-Stage Vehicle Type Recognition Method Combining the Most Effective Gabor Features

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Computers, Materials & Continua 2020年第12期65卷 2489-2510页

作者： Wei Sun Xiaorui Zhang Xiaozheng He Yan Jin Xu Zhang School of Automation Nanjing University of Information Science&TechnologyNanjing210044China Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology Nanjing University of Information Science&TechnologyNanjing210044China Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science&TechnologyNanjing210044China Rensselaer Polytechnic Institute TroyNY 12180USA

Vehicle type recognition(VTR)is an important research topic due to its significance in intelligent transportation ***,recognizing vehicle type on the real-world images is challenging due to the illumination change,partial occlusion under real traffic *** difficulties limit the performance of current state-of-art methods,which are typically based on single-stage classification without considering feature *** address such difficulties,this paper proposes a two-stage vehicle type recognition method combining the most effective Gabor *** first stage leverages edge features to classify vehicles by size into big or small via a similarity k-nearest neighbor classifier(SKNNC).Further the more specific vehicle type such as bus,truck,sedan or van is recognized by the second stage classification,which leverages the most effective Gabor features extracted by a set of Gabor wavelet kernels on the partitioned key patches via a kernel sparse representation-based classifier(KSRC).A verification and correction step based on minimum residual analysis is proposed to enhance the reliability of the *** improve VTR efficiency,the most effective Gabor features are selected through gray relational analysis that leverages the correlation between Gabor feature image and the original *** results demonstrate that the proposed method not only improves the accuracy of VTR but also enhances the recognition robustness to illumination change and partial occlusion.

关键词： Vehicle type recognition improved canny algorithm Gabor filter k-nearest neighbor classification grey relational analysis kernel sparse representation two-stage classification

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