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2nd IYSF Academic Symposium on Artificial Intelligence and Computer Engineering

作者： Wang, Dan Gao, Weixin Xian Shiyou Univ Coll Elect Engn Xian 710065 Peoples R China

ISBN: (数字)9781510650336

ISBN: (纸本)9781510650336;9781510650329

This paper summarizes the research results on defect segmentation and recognition by researchers at home and abroad. Through analysis, it is found that deep learning has an absolute advantage in seam defect segmentation based on X-ray. In the field of weld defect identification, the application of intelligent algorithm has become the mainstream of research.

关键词： Picture Processing defect segmentation defect Identification Intelligent Algorithm

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Self-adaptive characteristics segmentation optimized algorithm of weld defects based on flooding

Self-adaptive characteristics segmentation optimized algorit...

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Conference on Signal Processing, Sensor Fusion, and Target Recognition XXII

作者： Dang Changying Gao Jianmin Wang Zhao Chen Fumin Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710049 Shaanxi Peoples R China

ISBN: (纸本)9780819495365

In order to improve the accuracy and efficiency of weld defect segmentation in automatic radiographic nondestructive testing and evaluation(NDT&E), an effective weld defect segmentation algorithm based on flooding has been developed, which has the self-adaptive characteristics. Firstly, the defect's feature points are extracted from the scale space of radiographic films. Based on the information of defect points, the seed points and seed domains of defect discrimination are adaptively determined, in which the defect segmentation seed will be searched. Then, aiming at the sparsity of weld defects and canyon characteristics of 3D topographic map of defect regions, the drip-watering and water flooding have been used for reference. The flooding is carried out by using line-flooding algorithm, in which water starts from defect seed points and flows to the neighbor regions in order. On the basis of the flooding-area change and flooding-level ascending velocity, the defect segmentation threshold values are determined and the weld defects also are segmented from the radiographic films. At last, the comparative experiments have been carried out to compare the proposed algorithm with the watershed segmentation algorithm and background subtraction segmentation algorithm. And the experiment results confirm that the proposed algorithm obviously improves the accuracy and efficiency of weld defect's segmentation.

关键词： Radiographic testing defect segmentation flooding algorithm defect-seed point self-adaptive characteristics

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Surface defect Analysis of Rotary Metal Parts 13

Surface Defect Analysis of Rotary Metal Parts

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IEEE International Conference on Mechatronics and Automation

作者： Zhou, Awei Shao, Wei Xian Polytech Univ Coll Mech & Elect Engn Xian Shaanxi Provinc Peoples R China Xian Univ Technol Coll Mech & Precis Instrument Engn Xian Shaanxi Peoples R China

ISBN: (纸本)9781509023967

Surface defect analysis of rotary metal parts is a requirement to research quality and operating state of parts. This paper proposes a new approach and algorithms for surface defect analysis of rotary metal parts. Given the captured surface images as an input, the approach first uses the pose relation of the mechanism and a simple weighted average algorithm to obtain one seamless large image by fast stitching of the measured multi-view images. Then, to perform initial segmentation of surface defects, the possible defects are segmented using the developed segmentation algorithm based on contourlet transform with adaptive thresholds (CTAT) and the improved active contour model (ACM), and the defect parameters can be obtained and evaluated. Experimental results on inspection of the surface defects for a variety of rotary metal parts are reported to show the validity of the proposed methodology.

关键词： rotary metal parts defect analysis image stitching defect segmentation

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Polysilicon Slice Dislocation defects segmentation and Area Statistics based on Curve Fitting

Polysilicon Slice Dislocation Defects Segmentation and Area ...

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4th International Conference on Digital Manufacturing and Automation (ICDMA)

作者： Zhang, Wei-hua Liu, Tang-you DongHua Univ Coll Informat Sci & Technol Shanghai 200051 Peoples R China

ISBN: (纸本)9780769550169

The quality of the polysilicon slice affects the efficiency of polysilicon solar cells directly, dislocation defects exist in the polysilicon generally, a large number of dislocation defects have a greater impact on efficiency of solar cells. On the basis of photoluminescence defects detection, this paper proposes a new method that progressive scan image to obtain the grayscale curve of each row, and do curve fitting for each grayscale curve to achieve defect segmentation, by comparing the segmentation results obtained by quadratic curve and Gaussian curve fitting, proves that the quadratic curve fitting can be better for defects segmentation. At last, get the proportion of defective area in total slice area. Experiments show that the method of quadratic curve fitting is efficiency and accuracy for dislocation defects segmentation and counting defects area ratio.

关键词： Curve Fitting defect segmentation Area Statistics Dislocation defect

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Enhancement Of segmentation And Feature Fusion For Apple Disease Classification 10

Enhancement Of Segmentation And Feature Fusion For Apple Dis...

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10th International Conference on Advanced Computing (ICoAC)

作者： Sujatha, P. Kola Sandhya, J. Chaitanya, Jitta Sai Subashini, R. Anna Univ Dept Informat Technol Madras Inst Technol Campus Chennai Tamil Nadu India

ISBN: (纸本)9781728103532

Diseases in fruit cause serious problem in economic loss and production in agricultural industry. The quality and yield of the fruits can be degraded too much with the presence of the diseases in the fruit. It is critical to detect the diseases present in fruit and monitor fruit's health. Advanced computational methods have been used in agricultural applications, such as disease detection, classification and grading to help agriculturists in common farm tasks with more precision, efficiency, productivity and cost reduction. An approach for the apple disease classification with enhancement of defect segmentation and fusion of color, texture and shape based features is used to classify the apple into two types, healthy and defected with an accuracy of 96% using Histogram of Oriented Gradients feature descriptor and Bagged Decision Trees classifier.

关键词： Diseases defect segmentation feature extraction Histogram of Oriented Gradients feature fusion Bagged Decision Trees classification

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A collaborative inspection system composed of quadruped and flying robot for crack segmentation in tunnel environment

A collaborative inspection system composed of quadruped and ...

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ITA-AITES World Tunnel Congress (WTC) / 49th General Assembly of the International-Tunnelling-and-Underground-Space-Association (ITA-AITES)

作者： Chu, H. H. Cao, R. Deng, L. Hunan Univ Coll Civil Engn Changsha Peoples R China Hunan Univ Key Lab Damage Diag Engn Struct Hunan Prov Changsha Peoples R China

ISBN: (纸本)9781003348030

Advanced robotic systems involving unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) have shown advantages in detecting damaged infrastructures, such as bridges, buildings, and nuclear power plants. Due to their ability to leverage varied vantage points, a robot team of heterogeneous UAVs and UGVs could collect full space information of the structure with wider coverages and higher robustness compared with that from each robotic platform alone. However, critical issues still exist when applying the UAV-UGV inspection system in the tunnel environment. One major problem is that the lack of light and GPS signals may lead to a higher probability of UAV collisions. Another problem is that the road surface in the tunnel is generally uneven with many obstacles and puddles, which could hinder the travel of traditional wheeled or tracked UGVs. To address the above issues, a novel collaborative robotic system was proposed that consists of a quadruped robot with strong obstacle-striding abilities and a collision-resilient UAV. In the proposed inspection framework, the quadruped robot follows a pre-planned path and scans the tunnel lining with the vision sensor. Based on the scanning results, the UAV equipped with a protective cage would be launched from the helipad of the quadruped robot to perform a closer inspection. To correct its flight trajectory and protect the UAV, a GPS-free self-localization algorithm is constructed based on the data from the onboard computer, camera, and inertial measurement unit (IMU). Finally, to ensure that the tiny defects can be detected efficiently, a multi-scale feature fusion segmentation network with the attention mechanism was applied to images taken by the UAV. The performance of the system is validated against a field test, which demonstrates the feasibility of developing and deploying a collaborative inspection system using quadruped and flying robots for tunnel inspection.

关键词： Tunnel Inspection UAV UGV Collaboration Deep Learning Quadruped Robot defect segmentation

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Wavelet Entropy based Floating Thread defect Detection for Tufted Carpet

Wavelet Entropy based Floating Thread Defect Detection for T...

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2013 International Conference on Sport Material,Modelling and Simulation(ICSMMS 2013)

作者： Zhai Linlin Chen Guangfeng Engineering Research Center of Advanced Textile Machinery Ministry of Education Donghua University

ISBN: (纸本)9783037856635

This paper proposed the floating thread defect detection method base on wavelet entropy for tufted carpet. The method first get the image from cam and preprocesses the captured image with filtering, then using Wavelet Entropy to judge whether there are defects in the image. For image consist of defects, apply further thresholding operation to the image. Closing operation was adopted to eliminate noise and highlight the defects. At last, nine samples image were tested to verify the feasibility of proposed method.

关键词： Tufted carpet Floating thread defect detection Wavelet entropy defect segmentation

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Attention-Enhanced Co-Interactive Fusion Network (AECIF-Net) for automated structural condition assessment in visual inspection

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AUTOMATION IN CONSTRUCTION 2024年 159卷

作者： Zhang, Chenyu Yin, Zhaozheng Qin, Ruwen SUNY Stony Brook Dept Civil Engn 2427 Comp Sci Bldg Stony Brook NY 11794 USA SUNY Stony Brook Dept Comp Sci Dept Biomed Informat 2313B Comp Sci Bldg Stony Brook NY 11794 USA SUNY Stony Brook AI Inst 2313B Comp Sci Bldg Stony Brook NY 11794 USA

Efficiently monitoring the condition of civil infrastructure requires automating the structural condition assessment in visual inspection. This paper proposes an Attention -Enhanced Co -Interactive Fusion Network (AECIF-Net) for automatic structural condition assessment in visual bridge inspection. AECIF-Net can simultaneously parse structural elements and segment surface defects on the elements in inspection images. It integrates two task -specific relearning subnets to extract task -specific features from an overall feature embedding. A co -interactive feature fusion module further captures the spatial correlation and facilitates information sharing between tasks. Experimental results demonstrate that the proposed AECIF-Net outperforms the current state-of-the-art approaches, achieving promising performance with 92.11% mIoU for element segmentation and 87.16% mIoU for corrosion segmentation on the test set of the new benchmark dataset Steel Bridge Condition Inspection Visual (SBCIV). An ablation study verifies the merits of the designs for AECIF-Net, and a case study demonstrates its capability to automate structural condition assessment.

关键词： Infrastructure inspection Multi-task learning Spatial attention Structural element segmentation defect segmentation

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Multiple defects Inspection of Dam Spillway Surface Using Deep Learning and 3D Reconstruction Techniques

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BUILDINGS 2023年第2期13卷 285-285页

作者： Hong, Kunlong Wang, Hongguang Yuan, Bingbing Wang, Tianfu Chinese Acad Sci Shenyang Inst Automat State Key Lab Robot Chuangxin Rd 135 Shenyang 110016 Peoples R China Chinese Acad Sci Inst Robot & Intelligent Mfg Chuangxin Rd 135 Shenyang 110016 Peoples R China Univ Chinese Acad Sci Yuquan Rd 19 Beijing 100049 Peoples R China

After a lengthy period of scouring, the reinforced concrete surface of the dam spillway (i.e., drift spillways and flood discharge spillways) will suffer from deterioration and damage. Regular manual inspection is time-consuming and dangerous. This paper presents a robotic solution to detect automatically, count defect instance numbers, and reconstruct the surface of dam spillways by incorporating the deep learning method with a visual 3D reconstruction method. The lack of a real dam defect dataset and incomplete registration of minor defects on the 3D mesh model in fusion step are two challenges addressed in the paper. We created a multi-class semantic segmentation dataset of 1711 images (with resolutions of 848 x 480 and 1280 x 720 pixels) acquired by a wall-climbing robot, including cracks, erosion, spots, patched areas, and power safety cable. Then, the architecture of the U-net is modified with pixel-adaptive convolution (PAC) and conditional random field (CRF) to segment different scales of defects, trained, validated, and tested using this dataset. The reconstruction and recovery of minor defect instances in the flow surface and sidewall are facilitated using a keyframe back-projection method. By generating an instance adjacency matrix within the class, the intersection over union (IoU) of 3D voxels is calculated to fuse multiple instances. Our segmentation model achieves an average IoU of 60% for five defect class. For the surface model's semantic recovery and instance statistics, our method achieves accurate statistics of patched area and erosion instances in an environment of 200 m(2), and the average absolute error of the number of spots and cracks has reduced from the original 13.5 to 3.5.

关键词： visual inspection defect segmentation 3D reconstruction instance fusion defect counting

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Accurately identifying the defects of bubbles and foreign objects under the protective films of electric vehicle batteries by using 3D point clouds

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MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第9期35卷 095015-095015页

作者： Wu, Bingjie Bai, Yi Lv, Kun Zhang, Geyou Liu, Kai Sichuan Univ Coll Elect Engn Chengdu 610065 Sichuan Peoples R China Jiuquan Satellite Launch Ctr Jiuquan 735400 Gansu Peoples R China Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Sichuan Peoples R China

For the defects of bubbles and foreign objects under the protective film of electric vehicle batteries, it is difficult to accurately identify them over traditional 2D optical images. In this paper, we first propose a supervoxel-based region growing algorithm for pre-segmentation of point clouds. Secondly, we utilize radial basis function interpolation and threshold segmentation methods to accurately segment defect point clouds from the entire point cloud. Finally, we develop a feature descriptor and combine it with support vector machine to classify bubbles and foreign objects under the film. This paper achieves the identification of bubbles and foreign objects under the film through two steps: point cloud segmentation and point cloud classification. Experimental results demonstrate that the proposed point cloud segmentation method exhibits high robustness to noise and the intrinsic curvature of the workpiece. Additionally, in the classification scenario presented in this paper, the proposed feature descriptor outperforms classical feature descriptors. Compared to image-based deep learning methods, the defect recognition algorithm proposed in this paper has clear principles and superior performance, with precision and recall of 95.63% and 96.95%, and an intersection over union metric of 0.926.

关键词： electric vehicle battery 3D point cloud defect segmentation defect classification

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