TAS2-Net: Triple Attention Semantic Segmentation Network for Small Surface Defect Detection

2022 ◽  
pp. 1-1
Author(s):  
Taiheng Liu ◽  
Zhaoshui He
Keyword(s):  
Defect Detection ◽  
Surface Defect ◽  
Semantic Segmentation ◽  
Small Surface ◽  
Surface Defect Detection

scholarly journals Semantic Segmentation Network for Surface Defect Detection of Automobile Wheel Hub Fusing High-Resolution Feature and Multi-Scale Feature

2021 ◽  
Vol 11 (22) ◽  
pp. 10508
Author(s):  
Chaowei Tang ◽  
Xinxin Feng ◽  
Haotian Wen ◽  
Xu Zhou ◽  
Yanqing Shao ◽  
...  
Keyword(s):  
High Resolution ◽  
Defect Detection ◽  
Automobile Industry ◽  
Surface Defect ◽  
Semantic Segmentation ◽  
The Body ◽  
Multi Scale ◽  
Surface Defect Detection ◽  
Edge Features ◽  
Automobile Wheel

Surface defect detection of an automobile wheel hub is important to the automobile industry because these defects directly affect the safety and appearance of automobiles. At present, surface defect detection networks based on convolutional neural network use many pooling layers when extracting features, reducing the spatial resolution of features and preventing the accurate detection of the boundary of defects. On the basis of DeepLab v3+, we propose a semantic segmentation network for the surface defect detection of an automobile wheel hub. To solve the gridding effect of atrous convolution, the high-resolution network (HRNet) is used as the backbone network to extract high-resolution features, and the multi-scale features extracted by the Atrous Spatial Pyramid Pooling (ASPP) of DeepLab v3+ are superimposed. On the basis of the optical flow, we decouple the body and edge features of the defects to accurately detect the boundary of defects. Furthermore, in the upsampling process, a decoder can accurately obtain detection results by fusing the body, edge, and multi-scale features. We use supervised training to optimize these features. Experimental results on four defect datasets (i.e., wheels, magnetic tiles, fabrics, and welds) show that the proposed network has better F1 score, average precision, and intersection over union than SegNet, Unet, and DeepLab v3+, proving that the proposed network is effective for different defect detection scenarios.

scholarly journals Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks

2018 ◽  
Vol 8 (9) ◽  
pp. 1575 ◽  
Author(s):  
Xian Tao ◽  
Dapeng Zhang ◽  
Wenzhi Ma ◽  
Xilong Liu ◽  
De Xu
Keyword(s):  
Defect Detection ◽  
Surface Defect ◽  
Semantic Segmentation ◽  
Low Noise ◽  
Machine Learning Techniques ◽  
Metallic Surface ◽  
Defect Inspection ◽  
Learning Techniques ◽  
Defect Image ◽  
Surface Defect Detection

Automatic metallic surface defect inspection has received increased attention in relation to the quality control of industrial products. Metallic defect detection is usually performed against complex industrial scenarios, presenting an interesting but challenging problem. Traditional methods are based on image processing or shallow machine learning techniques, but these can only detect defects under specific detection conditions, such as obvious defect contours with strong contrast and low noise, at certain scales, or under specific illumination conditions. This paper discusses the automatic detection of metallic defects with a twofold procedure that accurately localizes and classifies defects appearing in input images captured from real industrial environments. A novel cascaded autoencoder (CASAE) architecture is designed for segmenting and localizing defects. The cascading network transforms the input defect image into a pixel-wise prediction mask based on semantic segmentation. The defect regions of segmented results are classified into their specific classes via a compact convolutional neural network (CNN). Metallic defects under various conditions can be successfully detected using an industrial dataset. The experimental results demonstrate that this method meets the robustness and accuracy requirements for metallic defect detection. Meanwhile, it can also be extended to other detection applications.

Positive-Sample-Based Surface Defect Detection Using Memory-Augmented Adversarial Autoencoders

2021 ◽  
pp. 1-1
Author(s):  
Tongzhi Niu ◽  
Bin Li ◽  
Weifeng Li ◽  
Yuanhong Qiu ◽  
Shuanlong Niu
Keyword(s):  
Defect Detection ◽  
Surface Defect ◽  
Positive Sample ◽  
Surface Defect Detection

SDDNet: A Fast and Accurate Network for Surface Defect Detection

2021 ◽  
Vol 70 ◽  
pp. 1-13
Author(s):  
Lisha Cui ◽  
Xiaoheng Jiang ◽  
Mingliang Xu ◽  
Wanqing Li ◽  
Pei Lv ◽  
...  
Keyword(s):  
Defect Detection ◽  
Surface Defect ◽  
Surface Defect Detection

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.

Sign in / Sign up

Export Citation Format

Share Document