Surface Defect Detection of Vehicle Light Guide Plates Based on an Improved RetinaNet

2021 ◽  
Author(s):  
Junfeng Li ◽  
Hao Wang
Keyword(s):  
Defect Detection ◽  
Semantic Information ◽  
Light Guide ◽  
Feature Fusion ◽  
Inspection Method ◽  
Aluminum Profile ◽  
Image Characteristics ◽  
Surface Defect Detection ◽  
Speed Up ◽  
High Level

Abstract Aiming at the vehicle navigation light guide plate (LGP) image characteristics, such as complex and gradient texures, uneven brightness, and small defects, this paper proposes a visual inspection method for LGP defects based on an improved RetinaNet. First, we use ResNeXt50 with higher accuracy under the same parameters as the backbone network, and propose the lightweight module Ghost_module to replace the 1×1 convolution in the lower half of the ResNeXt_block. This can reduce the resource parameters and consumption, and speed up training and inference. Second, we propose and use an improved feature pyramid network (IFPN) module to improve the feature fusion network in RetinaNet. It can more effectively fuse the shallow semantic information and high-level semantic information in the backbone feature extraction network, and further improve the detection ability of small target defects. Finally, the defect detection dataset constructed based on the vehicle LGP images collected at a industrial site, and experiments are performed on the vehicle LGP dataset and Aluminum Profile Defect Identification dataset (Aluminum Profile DID). The experimental results show that the proposed method is both efficient and effective. It achieves a better average detection rate of 98.6% on the vehicle LGP dataset. The accuracy and real-time performance can meet the requirements of industrial detection.

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.

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 Multilevel feature fusion dilated convolutional network for semantic segmentation

2021 ◽  
Vol 18 (2) ◽  
pp. 172988142110076
Author(s):  
Tao Ku ◽  
Qirui Yang ◽  
Hao Zhang
Keyword(s):  
Large Scale ◽  
Semantic Information ◽  
Spatial Information ◽  
Feature Fusion ◽  
Scene Perception ◽  
Semantic Segmentation ◽  
Field Size ◽  
Data Set ◽  
Dilated Convolution ◽  
High Level

Recently, convolutional neural network (CNN) has led to significant improvement in the field of computer vision, especially the improvement of the accuracy and speed of semantic segmentation tasks, which greatly improved robot scene perception. In this article, we propose a multilevel feature fusion dilated convolution network (Refine-DeepLab). By improving the space pyramid pooling structure, we propose a multiscale hybrid dilated convolution module, which captures the rich context information and effectively alleviates the contradiction between the receptive field size and the dilated convolution operation. At the same time, the high-level semantic information and low-level semantic information obtained through multi-level and multi-scale feature extraction can effectively improve the capture of global information and improve the performance of large-scale target segmentation. The encoder–decoder gradually recovers spatial information while capturing high-level semantic information, resulting in sharper object boundaries. Extensive experiments verify the effectiveness of our proposed Refine-DeepLab model, evaluate our approaches thoroughly on the PASCAL VOC 2012 data set without MS COCO data set pretraining, and achieve a state-of-art result of 81.73% mean interaction-over-union in the validate set.

scholarly journals PGA-Net: Pyramid Feature Fusion and Global Context Attention Network for Automated Surface Defect Detection

2020 ◽  
Vol 16 (12) ◽  
pp. 7448-7458 ◽  
Author(s):  
Hongwen Dong ◽  
Kechen Song ◽  
Yu He ◽  
Jing Xu ◽  
Yunhui Yan ◽  
...  
Keyword(s):  
Defect Detection ◽  
Surface Defect ◽  
Feature Fusion ◽  
Attention Network ◽  
Global Context ◽  
Surface Defect Detection

A surface defect detection method via fusing multi-level features

2022 ◽  
pp. 1-17
Author(s):  
Meijian Ren ◽  
Rulin Shen ◽  
Yanling Gong
Keyword(s):  
High Resolution ◽  
Defect Detection ◽  
Surface Defect ◽  
Detection Method ◽  
Feature Fusion ◽  
Size Difference ◽  
Feature Maps ◽  
Low Contrast ◽  
Surface Defect Detection ◽  
Multi Level

Abstract Surface defect detection is very important to ensure product quality, but most of the surface defects of industrial products are characterized by low contrast, big size difference and category similarity, which brings challenges to the automatic detection of defects. To solve these problems, we propose a defect detection method based on convolutional neural network. In this method, a backbone network with semantic supervision is applied to extract the features of different levels. While a multi-level feature fusion module is proposed to fuse adjacent feature maps into high-resolution feature maps successively, which significantly improves the prediction accuracy of the network. Finally, an Encoding module is used to obtain the global context information of the high-resolution feature map, which further improves the pixel classification accuracy. Experiments show that the proposed method is superior to other methods in NEU_SEG (mIoU of 85.27) and MT (mIoU of 77.82) datasets, and has the potential of real-time detection.

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