An improved detection method of metal product surface damage based on super-resolution reconstruction

2021 ◽  
Author(s):  
Yang Liu ◽  
Song Gao ◽  
Tengsen Wang ◽  
Huihui Wang ◽  
Qingjia Lv ◽  
...  
Keyword(s):  
Detection Method ◽  
Super Resolution ◽  
Surface Damage ◽  
Metal Product ◽  
Product Surface

scholarly journals Non-Temporal Point Cloud Analysis for Surface Damage in Civil Structures

2019 ◽  
Vol 8 (12) ◽  
pp. 527 ◽  
Author(s):  
Mohammad Ebrahim Mohammadi ◽  
Richard L. Wood ◽  
Christine E. Wittich
Keyword(s):  
Damage Detection ◽  
Real World ◽  
Point Cloud ◽  
Detection Method ◽  
False Positive Rate ◽  
Point Clouds ◽  
Surface Damage ◽  
Normal Surface ◽  
Synthetic Datasets

Assessment and evaluation of damage in civil infrastructure is most often conducted visually, despite its subjectivity and qualitative nature in locating and verifying damaged areas. This study aims to present a new workflow to analyze non-temporal point clouds to objectively identify surface damage, defects, cracks, and other anomalies based solely on geometric surface descriptors that are irrespective of point clouds’ underlying geometry. Non-temporal, in this case, refers to a single dataset, which is not relying on a change detection approach. The developed method utilizes vertex normal, surface variation, and curvature as three distinct surface descriptors to locate the likely damaged areas. Two synthetic datasets with planar and cylindrical geometries with known ground truth damage were created and used to test the developed workflow. In addition, the developed method was further validated on three real-world point cloud datasets using lidar and structure-from-motion techniques, which represented different underlying geometries and exhibited varying severity and mechanisms of damage. The analysis of the synthetic datasets demonstrated the robustness of the proposed damage detection method to classify vertices as surface damage with high recall and precision rates and a low false-positive rate. The real-world datasets illustrated the scalability of the damage detection method and its ability to classify areas as damaged and undamaged at the centimeter level. Moreover, the output classification of the damage detection method automatically bins the damaged vertices into different confidence intervals for further classification of detected likely damaged areas. Moving forward, the presented workflow can be used to bolster structural inspections by reducing subjectivity, enhancing reliability, and improving quantification in surface-evident damage.

Hardening of Metal Product Surface Layers by the Combined Gas Discharge Plasma

2017 ◽  
Vol 62 (12) ◽  
pp. 1856-1862 ◽  
Author(s):  
B. M. Brzhozovskii ◽  
S. G. Gestrin ◽  
E. P. Zinina ◽  
V. V. Martynov
Keyword(s):  
Discharge Plasma ◽  
Gas Discharge ◽  
Metal Product ◽  
Surface Layers ◽  
Gas Discharge Plasma ◽  
Product Surface

scholarly journals FOD Detection Method Based on Iterative Adaptive Approach for Millimeter-Wave Radar

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1241
Author(s):  
Yangliang Wan ◽  
Xingdong Liang ◽  
Xiangxi Bu ◽  
Yunlong Liu
Keyword(s):  
False Alarm ◽  
Millimeter Wave ◽  
Detection Method ◽  
Super Resolution ◽  
False Alarms ◽  
Target Information ◽  
Millimeter Wave Radar ◽  
Resolution Capability ◽  
Wave Radar ◽  
Iterative Adaptive Approach

Using millimeter-wave radar to scan and detect small foreign object debris (FOD) on an airport runway surface is a popular solution in civil aviation safety. Since it is impossible to completely eliminate the interference reflections arising from strongly scattering targets or non-homogeneous clutter after clutter cancellation processing, the consequent high false alarm probability has become a key problem to be solved. In this article, we propose a new FOD detection method for interference suppression and false alarm reduction based on an iterative adaptive approach (IAA) algorithm, which is a non-parametric, weighted least squares-based iterative adaptive processing approach that can provide super-resolution capability. Specifically, we first obtain coarse FOD target information by data preprocessing in a conventional detection method. Then, a refined data processing step is conducted based on the IAA algorithm in the azimuth direction. Finally, multiple pieces of information from the two steps above are used to comprehensively distinguish false alarms by fusion processing; thus, we can acquire accurate FOD target information. Real airport data measured by a 93 GHz radar are used to validate the proposed method. Experimental results of the test scene, which include golf balls with a diameter of 43 mm, were placed about 300 m away from radar, which show that the proposed method can effectively reduce the number of false alarms when compared with a traditional FOD detection method. Although metal balls with a diameter of 50 mm were placed about 660 m away from radar, they also can obtain up to 2.2 times azimuth super-resolution capability.

scholarly journals Research on insulator defect detection algorithm of transmission line based on CenterNet

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0255135
Author(s):  
Chunming Wu ◽  
Xin Ma ◽  
Xiangxu Kong ◽  
Haichao Zhu
Keyword(s):  
Transmission Line ◽  
Defect Detection ◽  
Detection Method ◽  
Detection Efficiency ◽  
Super Resolution ◽  
Reconstruction Algorithm ◽  
Detection Algorithm ◽  
Electric Power System ◽  
Stable Operation ◽  
Detection Accuracy

The reliability of the insulator has directly affected the stable operation of electric power system. The detection of defective insulators has always been an important issue in smart grid systems. However, the traditional transmission line detection method has low accuracy and poor real-time performance. We present an insulator defect detection method based on CenterNet. In order to improve detection efficiency, we simplified the backbone network. In addition, an attention mechanism is utilized to suppress useless information and improve the accuracy of network detection. In image preprocessing, the blurring of some detected images results in the samples being discarded, so we use super-resolution reconstruction algorithm to reconstruct the blurred images to enhance the dataset. The results show that the AP of the proposed method reaches 96.16% and the reasoning speed reaches 30FPS under the test condition of NVIDIA GTX 1080 test conditions. Compared with Faster R-CNN, YOLOV3, RetinaNet and FSAF, the detection accuracy of proposed method is greatly improved, which fully proves the effectiveness of the proposed method.

An enhanced fault-detection method based on adaptive spectral decomposition and super-resolution deep learning

2019 ◽  
Vol 7 (3) ◽  
pp. T713-T725
Author(s):  
Zhenyu Yuan ◽  
Handong Huang ◽  
Yuxin Jiang ◽  
Jinbiao Tang ◽  
Jingjing Li
Keyword(s):  
Deep Learning ◽  
Fault Detection ◽  
Spectral Decomposition ◽  
Detection Method ◽  
Eastern China ◽  
Amplitude Spectrum ◽  
Super Resolution ◽  
Dominant Frequency ◽  
Seismic Survey ◽  
Amplitude Data

Coherence is widely used for detecting faults in reservoir characterization. However, faults detected from coherence may be contaminated by some other discontinuities (e.g., noise and stratigraphic features) that are unrelated to faults. To further improve the accuracy and efficiency of coherence, preprocessing or postprocessing techniques are required. We developed an enhanced fault-detection method with adaptive scale highlighting and high resolution, by combining adaptive spectral decomposition and super-resolution (SR) deep learning into coherence calculation. As a preprocessing technique, adaptive spectral decomposition is first proposed and applied on seismic data to get a dominant-frequency-optimized amplitude spectrum, which has features of scale focus and multiple resolution. Eigenstructure-based coherence with dip correction is then calculated to delineate fault discontinuities. Following the remarkable success of SR deep learning in image reconstruction, a convolutional neural network (CNN) model is built and it then takes fault-detection images as the input to achieve enhanced results. The effectiveness of our proposed method is validated on a seismic survey acquired from Eastern China. Examples demonstrate that coherence from adaptive amplitude spectrum without dip correction is comparable to the dip-corrected one from seismic amplitude data at a certain degree, and they even highlight the specific scale of fault targets. Comparing fault detections from adaptive spectrum and some specific-frequency components, it can be concluded that adaptive spectral-based coherence highlights the primary scale of faults at various depths with only one single volume of data, thus improving the interpretation efficiency and reducing storage cost. Furthermore, with the trained CNN model, the resolution and signal-to-noise ratio of coherence images are effectively improved and the continuity of detected fault is promisingly enhanced.

scholarly journals Target Detection Method for Low-Resolution Remote Sensing Image Based on ESRGAN and ReDet

Photonics ◽  
2021 ◽  
Vol 8 (10) ◽  
pp. 431
Author(s):  
Yuwu Wang ◽  
Guobing Sun ◽  
Shengwei Guo
Keyword(s):  
Remote Sensing ◽  
Target Detection ◽  
Detection Method ◽  
Recognition Rate ◽  
Super Resolution ◽  
Remote Sensing Image ◽  
Remote Sensing Images ◽  
Low Resolution ◽  
Generative Adversarial Network ◽  
Adversarial Network

With the widespread use of remote sensing images, low-resolution target detection in remote sensing images has become a hot research topic in the field of computer vision. In this paper, we propose a Target Detection on Super-Resolution Reconstruction (TDoSR) method to solve the problem of low target recognition rates in low-resolution remote sensing images under foggy conditions. The TDoSR method uses the Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) to perform defogging and super-resolution reconstruction of foggy low-resolution remote sensing images. In the target detection part, the Rotation Equivariant Detector (ReDet) algorithm, which has a higher recognition rate at this stage, is used to identify and classify various types of targets. While a large number of experiments have been carried out on the remote sensing image dataset DOTA-v1.5, the results of this paper suggest that the proposed method achieves good results in the target detection of low-resolution foggy remote sensing images. The principal result of this paper demonstrates that the recognition rate of the TDoSR method increases by roughly 20% when compared with low-resolution foggy remote sensing images.

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