Novel Feature Fusion Module Based Detector for Small Insulator Defect Detection

2021 ◽  
pp. 1-1
Author(s):  
Zishu Gao ◽  
Guodong Yang ◽  
En Li ◽  
Zize Liang
Keyword(s):  
Defect Detection ◽  
Feature Fusion

Improved SSD-assisted algorithm for surface defect detection of electromagnetic luminescence

2021 ◽  
pp. 1748006X2199538
Author(s):  
Zhenying Xu ◽  
Ziqian Wu ◽  
Wei Fan
Keyword(s):  
Defect Detection ◽  
Feature Fusion ◽  
Recognition Rate ◽  
Detection Methods ◽  
Small Scale ◽  
Detection Accuracy ◽  
Single Shot ◽  
Surface Defect Detection ◽  
Feature Pyramid ◽  
Small Feature

Defect detection of electromagnetic luminescence (EL) cells is the core step in the production and preparation of solar cell modules to ensure conversion efficiency and long service life of batteries. However, due to the lack of feature extraction capability for small feature defects, the traditional single shot multibox detector (SSD) algorithm performs not well in EL defect detection with high accuracy. Consequently, an improved SSD algorithm with modification in feature fusion in the framework of deep learning is proposed to improve the recognition rate of EL multi-class defects. A dataset containing images with four different types of defects through rotation, denoising, and binarization is established for the EL. The proposed algorithm can greatly improve the detection accuracy of the small-scale defect with the idea of feature pyramid networks. An experimental study on the detection of the EL defects shows the effectiveness of the proposed algorithm. Moreover, a comparison study shows the proposed method outperforms other traditional detection methods, such as the SIFT, Faster R-CNN, and YOLOv3, in detecting the EL defect.

Sewer pipe defect detection via deep learning with local and global feature fusion

2021 ◽  
Vol 129 ◽  
pp. 103823
Author(s):  
Dawei Li ◽  
Qian Xie ◽  
Zhenghao Yu ◽  
Qiaoyun Wu ◽  
Jun Zhou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Defect Detection ◽  
Feature Fusion ◽  
Global Feature ◽  
Sewer Pipe

Attention U-Net with Feature Fusion Module for Robust Defect Detection

2021 ◽  
pp. 2150272
Author(s):  
Yu-Jie Xiong ◽  
Yong-Bin Gao ◽  
Hong Wu ◽  
Yao Yao
Keyword(s):  
Defect Detection ◽  
Crack Detection ◽  
Feature Fusion ◽  
Experimental Results ◽  
Feature Maps ◽  
Concrete Crack ◽  
Disaster Situation ◽  
Unpredictable Environment ◽  
Segmentation Task ◽  
Noisy Background

U-Net shows a remarkable performance and makes significant progress for segmentation task in medical images. Despite the outstanding achievements, the common case of defect detection in industrial scenes is still a challenging task, due to the noisy background, unpredictable environment, varying shapes and sizes of the defects. Traditional U-Net may not be suitable for low-quality images with low illumination and corruption, which are often presented in the practical collections in real-world scenes. In this paper, we propose an attention U-Net with feature fusion module for combining multi-scale features to detect the defects in noisy images automatically. Feature fusion module contains convolution kernels of different scales to capture shallow layer features and combine them with the high-dimensional features. Meanwhile, attention gates are used to enhance the robustness of skip connection between the feature maps. The proposed method is evaluated on two datasets. The best precision rate and MIoU of defect detection are 95.6% and 92.5%. The best F-score of concrete crack detection is 95.0%. Experimental results show that the proposed approach achieves promising results in both datasets. It demonstrates that our approach consistently outperforms other U-Net-based approaches for defect detection in low-quality images. Experimental results have shown the possibility of developing a mixture system that can be deployed in many applications, such as remote sensing image analysis, earthquake disaster situation assessment, and so on.

A surface defect detection method based on multi-feature fusion

2017 ◽  
Author(s):  
Xiaojun Wu ◽  
Huijiang Xiong ◽  
Zhiyang Yu ◽  
Peizhi Wen
Keyword(s):  
Defect Detection ◽  
Surface Defect ◽  
Detection Method ◽  
Feature Fusion ◽  
Surface Defect Detection

scholarly journals BLNN: Multiscale Feature Fusion-Based Bilinear Fine-Grained Convolutional Neural Network for Image Classification of Wood Knot Defects

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Mingyu Gao ◽  
Fei Wang ◽  
Peng Song ◽  
Junyan Liu ◽  
DaWei Qi
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Defect Detection ◽  
Feature Fusion ◽  
Image Features ◽  
Optical Image ◽  
Training Time ◽  
Fine Grained ◽  
Detection Model ◽  
Defect Recognition

Wood defects are quickly identified from an optical image based on deep learning methodology, which effectively improves the wood utilization. The traditional neural network technique is unemployed for the wood defect detection of optical image used, which results from a long training time, low recognition accuracy, and nonautomatic extraction of defect image features. In this paper, a wood knot defect detection model (so-called BLNN) combined deep learning is reported. Two subnetworks composed of convolutional neural networks are trained by Pytorch. By using the feature extraction capabilities of the two subnetworks and combining the bilinear join operation, the fine-grained features of the image are obtained. The experimental results show that the accuracy has reached up 99.20%, and the training time is obviously reduced with the speed of defect detection about 0.0795 s/image. It indicates that BLNN has the ability to improve the accuracy of defect recognition and has a potential application in the detection of wood knot defects.

Сочетание вручную построенных признаков и признаков глубоких сетей для автоматического обнаружеия дефектов в процессе визуализации квадратичной частотно-модулированной тепловой волны

2021 ◽  
Vol 6 ◽  
pp. 46-56
Author(s):  
Г.Т. Весала ◽  
В.С. Гали ◽  
А. Виджая Лакшми Лакшми ◽  
Р.Б. Найк
Keyword(s):  
Neural Network ◽  
Defect Detection ◽  
Feature Fusion ◽  
Signal To Noise Ratio ◽  
Semantic Segmentation ◽  
Object Localization ◽  
Destructive Testing ◽  
Global Features ◽  
Flat Bottom ◽  
Local Networks

Recent advancements of non-destructive testing and evaluation (NDT&E) reached the fourth revolution with machine learning, artificial intelligence, and the internet of things as key enablers in parallel with industry 4.0. Nevertheless, Active thermography (AT) is a non-contact, whole field, safe, remote, cost-efficient, and widely used NDT technique for subsurface anomaly detection. In AT, the automatic defect detection is modelled as object localization and semantic segmentation in thermograms. This paper presents a feature fusion network that fuses the global features extracted using a deep neural network (DNN) with the deep features extracted using a convolutional neural network (CNN). A set of handcrafted time-domain statistical and frequency domain features of thermal profiles are given to the DNN sub-network whereas, the CNN sub-network is fed with the thermal profiles in the feature fusion network. Experimentation is carried out over carbon fiber reinforced polymer (CFRP) sample with artificially drilled flat bottom holes excited by quadratic frequency-modulated optical stimulus. Experimental results showed that the feature fusion enhanced the defect detection capability compared to the local networks with a significant increment in signal-to-noise ratio, accuracy, and F-score.

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