scholarly journals The Development of a Defect Detection Model from the High-Resolution Images of a Sugarcane Plantation Using an Unmanned Aerial Vehicle

Information ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 136
Author(s):  
Bhoomin Tanut ◽  
Panomkhawn Riyamongkol
Keyword(s):  
Feature Extraction ◽  
High Resolution ◽  
Defect Detection ◽  
Extraction Methods ◽  
Detection Algorithm ◽  
K Nearest Neighbors ◽  
Color Selection ◽  
Detection Model ◽  
Aerial Vehicle ◽  
Processing Techniques

This article presents a defect detection model of sugarcane plantation images. The objective is to assess the defect areas occurring in the sugarcane plantation before the harvesting seasons. The defect areas in the sugarcane are usually caused by storms and weeds. This defect detection algorithm uses high-resolution sugarcane plantations and image processing techniques. The algorithm for defect detection consists of four processes: (1) data collection, (2) image preprocessing, (3) defect detection model creation, and (4) application program creation. For feature extraction, the researchers used image segmentation and convolution filtering by 13 masks together with mean and standard deviation. The feature extraction methods generated 26 features. The K-nearest neighbors algorithm was selected to develop a model for the classification of the sugarcane areas. The color selection method was also chosen to detect defect areas. The results show that the model can recognize and classify the characteristics of the objects in sugarcane plantation images with an accuracy of 96.75%. After the comparison with the expert surveyor’s assessment, the accurate relevance obtained was 92.95%. Therefore, the proposed model can be used as a tool to calculate the percentage of defect areas and solve the problem of evaluating errors of yields in the future.

scholarly journals Detection method based on improved faster R-CNN for pin defect in transmission lines

2021 ◽  
Vol 300 ◽  
pp. 01011
Author(s):  
Jun Wu ◽  
Sheng Cheng ◽  
Shangzhi Pan ◽  
Wei Xin ◽  
Liangjun Bai ◽  
...  
Keyword(s):  
Transmission Line ◽  
Defect Detection ◽  
Transmission Lines ◽  
False Positive Rate ◽  
Detection Algorithm ◽  
Detection Model ◽  
Positive Rate ◽  
Aerial Vehicle ◽  
The Stability ◽  
Small Targets

Defects such as insulator, pins, and counterweight in highvoltage transmission lines affect the stability of the power system. The small targets such as pins in the unmanned aerial vehicle (UAV) inspection images of transmission lines occupy a small proportion in the images and the characteristic representations are poor which results a low defect detection rate and a high false positive rate. This paper proposed a transmission line pin defect detection algorithm based on improved Faster R-CNN. First, the pre-training weights with higher matching degree are obtained based on transfer learning. And it is applied to construct defect detection model. Then, the regional proposal network is used to extract features in the model. The results of defect detection are obtained by regression calculation and classification of regional characteristics. The experimental results show that the accuracy of the pin defect detection of the transmission line reaches 81.25%

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 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.

scholarly journals Analyzing the Effectiveness of the Brain–Computer Interface for Task Discerning Based on Machine Learning

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2403
Author(s):  
Jakub Browarczyk ◽  
Adam Kurowski ◽  
Bozena Kostek
Keyword(s):  
Feature Extraction ◽  
Principal Component ◽  
Component Analysis ◽  
Mental States ◽  
Extraction Methods ◽  
Support Vector ◽  
Discrete Wavelet ◽  
K Nearest Neighbors ◽  
Vector Machines

The aim of the study is to compare electroencephalographic (EEG) signal feature extraction methods in the context of the effectiveness of the classification of brain activities. For classification, electroencephalographic signals were obtained using an EEG device from 17 subjects in three mental states (relaxation, excitation, and solving logical task). Blind source separation employing independent component analysis (ICA) was performed on obtained signals. Welch’s method, autoregressive modeling, and discrete wavelet transform were used for feature extraction. Principal component analysis (PCA) was performed in order to reduce the dimensionality of feature vectors. k-Nearest Neighbors (kNN), Support Vector Machines (SVM), and Neural Networks (NN) were employed for classification. Precision, recall, F1 score, as well as a discussion based on statistical analysis, were shown. The paper also contains code utilized in preprocessing and the main part of experiments.

scholarly journals Defect Detection of Pandrol Track Fastener Based on Local Depth Feature Fusion Network

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Zhaomin Lv ◽  
Anqi Ma ◽  
Xingjie Chen ◽  
Shubin Zheng
Keyword(s):  
Defect Detection ◽  
Feature Fusion ◽  
Texture Features ◽  
Extraction Methods ◽  
Detection Model ◽  
Deep Feature ◽  
Detection Capabilities ◽  
Edge Features ◽  
Depth Feature ◽  
Local Depth

There are three main problems in track fastener defect detection based on image: (1) The number of abnormal fastener pictures is scarce, and supervised learning detection model is difficult to establish. (2) The potential data features obtained by different feature extraction methods are different. Some methods focus on edge features, and some methods focus on texture features. Different features have different detection capabilities, and these features are not effectively fused and utilized. (3) The detection of the track fastener clip will be interfered by the track fastener bolt subimage. Aiming at the above three problems, a method for track fastener defects detection based on Local Deep Feature Fusion Network (LDFFN) is proposed. Firstly, the track fastener image segmentation method is used to obtain the track fastener clip subimage, which can effectively reduce the interference of bolt subimage features on the track fastener clip detection. Secondly, the edge features and texture features of track fastener clip subimages are extracted by Autoencoder (AE) and Restricted Boltzmann Machine (RBM), and the features are fused. Finally, the similarity measurement method Mahalanobis Distance (MD) is used to detect defects in track fasteners. The effectiveness of the proposed method is verified by real Pandrol track fastener images.

scholarly journals Disease Detection of Dragon Fruit Stem Based on The Combined Features of Color and Texture

2021 ◽  
Vol 5 (2) ◽  
pp. 161-175
Author(s):  
Lutfi Hakim ◽  
Sepyan Purnama Kristanto ◽  
Dianni Yusuf ◽  
Mohammad Nur Shodiq ◽  
Wahyu Ade Setiawan
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Texture Feature ◽  
Extraction Methods ◽  
Support Vector ◽  
Identification System ◽  
Accuracy Score ◽  
K Nearest Neighbors ◽  
Dragon Fruit ◽  
Combined Features

Dragon fruit is one of the favorite commodities in Banyuwangi Regency's agriculture. In 2019, this commodity had the fourth largest harvest area among other fruit commodities in Banyuwangi until it was exported to China. However, disease attacks often appeared in several dragon fruit plantations in Banyuwangi, and the identification system was still conventional. Many farmers did not know the types of disease and how to handle it, causing the quality and quantity of their crops to decline. Therefore, this study implemented two feature extraction methods. Both methods include color feature extraction using the color moments method and texture feature extraction using gray level co-occurrence matrices (GLCM). The methods used to develop a system that recognized or detected the three types of dragon fruit stem based on digital image processing using Support Vector Machine and k-Nearest Neighbors methods as comparison methods. The results obtained from this study indicated that the combination of the two proposed feature extraction methods could distinguish between stem rot, smallpox, and insect stings with an optimal accuracy score of 87.5% obtained by using Support Vector Machine as a classification method.

scholarly journals Automatic Feature Extraction and Construction Using Genetic Programming for Rotating Machinery Fault Diagnosis

2021 ◽  
Author(s):  
B Peng ◽  
S Wan ◽  
Ying Bi ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Genetic Programming ◽  
Domain Knowledge ◽  
Rolling Bearing ◽  
Extraction Methods ◽  
Rotating Machinery ◽  
K Nearest Neighbors ◽  
Automatic Feature Extraction ◽  
Diagnosis Approach

IEEE Feature extraction is an essential process in the intelligent fault diagnosis of rotating machinery. Although existing feature extraction methods can obtain representative features from the original signal, domain knowledge and expert experience are often required. In this article, a novel diagnosis approach based on evolutionary learning, namely, automatic feature extraction and construction using genetic programming (AFECGP), is proposed to automatically generate informative and discriminative features from original vibration signals for identifying different fault types of rotating machinery. To achieve this, a new program structure, a new function set, and a new terminal set are developed in AFECGP to allow it to detect important subband signals and extract and construct informative features, automatically and simultaneously. More important, AFECGP can produce a flexible number of features for classification. Having the generated features, k-Nearest Neighbors is employed to perform fault diagnosis. The performance of the AFECGP-based fault diagnosis approach is evaluated on four fault diagnosis datasets of varying difficulty and compared with 14 baseline methods. The results show that the proposed approach achieves better fault diagnosis accuracy on all the datasets than the competitive methods and can effectively identify different fault conditions of rolling bearing, gear, and rotor.

scholarly journals Automatic Feature Extraction and Construction Using Genetic Programming for Rotating Machinery Fault Diagnosis

2021 ◽  
Author(s):  
B Peng ◽  
S Wan ◽  
Ying Bi ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Genetic Programming ◽  
Domain Knowledge ◽  
Rolling Bearing ◽  
Extraction Methods ◽  
Rotating Machinery ◽  
K Nearest Neighbors ◽  
Automatic Feature Extraction ◽  
Diagnosis Approach

IEEE Feature extraction is an essential process in the intelligent fault diagnosis of rotating machinery. Although existing feature extraction methods can obtain representative features from the original signal, domain knowledge and expert experience are often required. In this article, a novel diagnosis approach based on evolutionary learning, namely, automatic feature extraction and construction using genetic programming (AFECGP), is proposed to automatically generate informative and discriminative features from original vibration signals for identifying different fault types of rotating machinery. To achieve this, a new program structure, a new function set, and a new terminal set are developed in AFECGP to allow it to detect important subband signals and extract and construct informative features, automatically and simultaneously. More important, AFECGP can produce a flexible number of features for classification. Having the generated features, k-Nearest Neighbors is employed to perform fault diagnosis. The performance of the AFECGP-based fault diagnosis approach is evaluated on four fault diagnosis datasets of varying difficulty and compared with 14 baseline methods. The results show that the proposed approach achieves better fault diagnosis accuracy on all the datasets than the competitive methods and can effectively identify different fault conditions of rolling bearing, gear, and rotor.

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