Fast detection algorithm for surface defects of metal parts based on YOLOv4-mobilenet network

2021 ◽  
Author(s):  
Shihe Guo ◽  
Peisi Zhong ◽  
Yongpeng Sun ◽  
Liang Li ◽  
Chao Zhang
Keyword(s):  
Surface Defects ◽  
Detection Algorithm ◽  
Fast Detection ◽  
Metal Parts

Lightweight detection algorithm for fine-grained surface defects of aerospace seal rings

2021 ◽  
pp. 1-18
Author(s):  
Hui Liu ◽  
Boxia He ◽  
Yong He ◽  
Xiaotian Tao
Keyword(s):  
Feature Extraction ◽  
Defect Detection ◽  
Surface Defect ◽  
Surface Defects ◽  
Detection Algorithm ◽  
Seal Ring ◽  
Fine Grained ◽  
Aerospace Applications ◽  
Surface Defect Detection ◽  
The Mean

The existing seal ring surface defect detection methods for aerospace applications have the problems of low detection efficiency, strong specificity, large fine-grained classification errors, and unstable detection results. Considering these problems, a fine-grained seal ring surface defect detection algorithm for aerospace applications is proposed. Based on analysis of the stacking process of standard convolution, heat maps of original pixels in the receptive field participating in the convolution operation are quantified and generated. According to the generated heat map, the feature extraction optimization method of convolution combinations with different dilation rates is proposed, and an efficient convolution feature extraction network containing three kinds of dilated convolutions is designed. Combined with the O-ring surface defect features, a multiscale defect detection network is designed. Before the head of multiscale classification and position regression, feature fusion tree modules are added to ensure the reuse and compression of the responsive features of different receptive fields on the same scale feature maps. Experimental results show that on the O-rings-3000 testing dataset, the mean condition accuracy of the proposed algorithm reaches 95.10% for 5 types of surface defects of aerospace O-rings. Compared with RefineDet, the mean condition accuracy of the proposed algorithm is only reduced by 1.79%, while the parameters and FLOPs are reduced by 35.29% and 64.90%, respectively. Moreover, the proposed algorithm has good adaptability to image blur and light changes caused by the cutting of imaging hardware, thus saving the cost.

scholarly journals A Fast Detection Algorithm for the X-Ray Pulsar Signal

2017 ◽  
Vol 2017 ◽  
pp. 1-5 ◽  
Author(s):  
Hao Liang ◽  
Yafeng Zhan
Keyword(s):  
False Alarm ◽  
Navigation System ◽  
Detection Probability ◽  
Autonomous Navigation ◽  
Detection Algorithm ◽  
Observation Time ◽  
X Ray ◽  
Fast Detection ◽  
Simulation Results ◽  
Short Observation Time

The detection of the X-ray pulsar signal is important for the autonomous navigation system using X-ray pulsars. In the condition of short observation time and limited number of photons for detection, the noise does not obey the Gaussian distribution. This fact has been little considered extant. In this paper, the model of the X-ray pulsar signal is rebuilt as the nonhomogeneous Poisson distribution and, in the condition of a fixed false alarm rate, a fast detection algorithm based on maximizing the detection probability is proposed. Simulation results show the effectiveness of the proposed detection algorithm.

Using Symmetry to Reduce Planning Complexity: A Case Study in Sheet-Metal Production

1997 ◽  
Author(s):  
Cheng-Hua Wang ◽  
David A. Bourne
Keyword(s):  
Sheet Metal ◽  
Detection Algorithm ◽  
Assembly Planning ◽  
Product Decomposition ◽  
Metal Production ◽  
Layout Planning ◽  
Significant Percentage ◽  
Metal Parts ◽  
Sheet Metal Parts

Abstract In this paper, we present an approach to recognize symmetries of bent sheet-metal parts at different manufacturing stages. This approach is based on Waltzman’s (Waltzman, 1987) 2D symmetry detection algorithm. 3D symmetry is recognized by considering its 2D symmetry and the associated bending transformations. We show, by recognizing that the part is symmetrical, that the planning complexity for processes in sheet-metal production can be greatly reduced. This paper is motivated by the fact that a significant percentage of sheet-metal parts are symmetrical. We have studied over 200 industrial parts and over 40% of them are symmetrical. Examples from sheet-metal nesting (layout planning), bending, stacking, product decomposition and assembly planning are discussed.

scholarly journals Research on a Surface Defect Detection Algorithm Based on MobileNet-SSD

2018 ◽  
Vol 8 (9) ◽  
pp. 1678 ◽  
Author(s):  
Yiting Li ◽  
Haisong Huang ◽  
Qingsheng Xie ◽  
Liguo Yao ◽  
Qipeng Chen
Keyword(s):  
Defect Detection ◽  
Surface Defect ◽  
Surface Defects ◽  
Detection Method ◽  
Detection Algorithm ◽  
Single Shot ◽  
Detection Model ◽  
Machine Learning Methods ◽  
Network Methods ◽  
Surface Defect Detection

This paper aims to achieve real-time and accurate detection of surface defects by using a deep learning method. For this purpose, the Single Shot MultiBox Detector (SSD) network was adopted as the meta structure and combined with the base convolution neural network (CNN) MobileNet into the MobileNet-SSD. Then, a detection method for surface defects was proposed based on the MobileNet-SSD. Specifically, the structure of the SSD was optimized without sacrificing its accuracy, and the network structure and parameters were adjusted to streamline the detection model. The proposed method was applied to the detection of typical defects like breaches, dents, burrs and abrasions on the sealing surface of a container in the filling line. The results show that our method can automatically detect surface defects more accurately and rapidly than lightweight network methods and traditional machine learning methods. The research results shed new light on defect detection in actual industrial scenarios.

Multipose measurement of surface defects on rotary metal parts with a combined laser-and-camera sensor

2013 ◽  
Vol 52 (10) ◽  
pp. 104104 ◽  
Author(s):  
Awei Zhou ◽  
Junjie Guo ◽  
Wei Shao ◽  
Jiao Yang
Keyword(s):  
Surface Defects ◽  
Metal Parts ◽  
Camera Sensor

scholarly journals A practical approach to tramway track condition monitoring: vertical track defects detection and identification using time-frequency processing technique

2018 ◽  
Vol 13 (s1) ◽  
pp. 135-146
Author(s):  
Péter Bocz ◽  
Ákos Vinkó ◽  
Zoltán Posgay
Keyword(s):  
Surface Defects ◽  
Detection Algorithm ◽  
Processing Technique ◽  
False Alarms ◽  
Distribution Analysis ◽  
Time Frequency ◽  
Detection And Identification ◽  
Track Condition ◽  
Automatic Detection Algorithm ◽  
Track Irregularities

Abstract This paper presents an automatic method for detecting vertical track irregularities on tramway operation using acceleration measurements on trams. For monitoring of tramway tracks, an unconventional measurement setup is developed, which records the data of 3-axes wireless accelerometers mounted on wheel discs. Accelerations are processed to obtain the vertical track irregularities to determine whether the track needs to be repaired. The automatic detection algorithm is based on time–frequency distribution analysis and determines the defect locations. Admissible limits (thresholds) are given for detecting moderate and severe defects using statistical analysis. The method was validated on frequented tram lines in Budapest and accurately detected severe defects with a hit rate of 100%, with no false alarms. The methodology is also sensitive to moderate and small rail surface defects at the low operational speed.

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