scholarly journals Research on Surface Defect Detection of Rare-Earth Magnetic Materials Based on Improved SSD

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Bin Zhang ◽  
Shuqi Fang ◽  
Zhixi Li
Keyword(s):  
Rare Earth ◽  
Magnetic Materials ◽  
Production Efficiency ◽  
Surface Defects ◽  
Feature Fusion ◽  
Single Shot ◽  
Increase Production Efficiency ◽  
Surface Defect Detection ◽  
Feature Pyramid ◽  
High Level

In order to overcome the limitation of manual visual inspection of surface defects of rare-earth magnetic materials and increase production efficiency of traditional rare-earth enterprises, a detection method based on improved SSD (Single Shot Detector) is proposed. The SSD model is improved from two aspects for better performance in the detection of small defects. First of all, the multiscale receptive field module is embedded into the backbone network of the algorithm to improve the feature extraction ability of the model. Secondly, the interlayer feature fusion strategy of bidirectional feature pyramid in PANet (path aggregation network) is integrated into the model. In order to enhance the detection ability of the model, the high-level semantic information is strengthened by an efficient channel attention mechanism. The detection speed of the improved SSD algorithm is 55FPS, and the mAP (mean Average Precision) is up to 83.65%, which is 3.41% higher than of the original SSD algorithm, and the ability to identify small defects is significantly improved.

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.

scholarly journals Detection of Various Types of Metal Surface Defects Based on Image Processing

2021 ◽  
Vol 38 (4) ◽  
pp. 1071-1078
Author(s):  
Peng Xue ◽  
Changhong Jiang ◽  
Huanli Pang
Keyword(s):  
Image Processing ◽  
Metal Surface ◽  
Production Efficiency ◽  
Surface Defects ◽  
Detection Algorithm ◽  
Production Equipment ◽  
Single Shot ◽  
Target Image ◽  
Nonlocal Means ◽  
Surface Defect Detection

Machine vision is a promising technique to promote intelligent production. It strikes a balance between product quality and production efficiency. However, the existing metal surface defect detection algorithms are too general, and deviate from electrical production equipment in the level of response time to the target image. To address the two problems, this paper designs a detection algorithm for various types of metal surface defects based on image processing. Firstly, each metal surface image was preprocessed through average graying and nonlocal means filtering. Next, the principle of the composite model scale expansion was explained, and an improved EfficientNet was constructed to classify metal surface defects, which couples spatial attention mechanism. Finally, the backbone network of the single shot multi-box detector (SSD) network was improved, and used to fuse the features of the target image. The proposed model was proved effective through experiments.

Steel Surface Defect Detection Using an Ensemble of Deep Residual Neural Networks

2021 ◽  
pp. 1-8
Author(s):  
Ihor Konovalenko ◽  
Pavlo Maruschak ◽  
Vitaly Brevus
Keyword(s):  
Neural Networks ◽  
Steel Surface ◽  
Production Efficiency ◽  
Surface Defects ◽  
Model Parameters ◽  
Feature Maps ◽  
Defect Diagnostics ◽  
Neuron Activation ◽  
Surface Defect Detection

Abstract Steel defect diagnostics is important for industry task as it is tied to the product quality and production efficiency. The aim of this paper is evaluating the application of residual neural networks for recognition of industrial steel defects of three classes. Developed and investigated models based on deep residual neural networks for the recognition and classification of surface defects of rolled steel. Investigated the influence of various loss functions, optimizers and hyperparameters on the obtained result and selected optimal model parameters. Based on an ensemble of two deep residual neural networks ResNet50 and ResNet152, a classifier was constructed to detect defects of three classes on flat metal surfaces. The proposed technique allows classifying images with high accuracy. The average binary accuracy of classifying the test data is 96.7% for all images (including defect-free ones). The fields of neuron activation in the convolutional layers of the model were investigated. Feature maps formed in the process were found to reflect the position, size and shape of the objects of interest very well. The proposed ensemble model has proven to be robust and able to accurately recognize steel surface defects. Erroneous recognition cases of the classifier application are investigated. It was shown that errors most often occur in ambiguous situations, where surface artifacts of different types are similar.

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.

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.

scholarly journals M2Det: A Single-Shot Object Detector Based on Multi-Level Feature Pyramid Network

2019 ◽  
Vol 33 ◽  
pp. 9259-9266 ◽  
Author(s):  
Qijie Zhao ◽  
Tao Sheng ◽  
Yongtao Wang ◽  
Zhi Tang ◽  
Ying Chen ◽  
...  
Keyword(s):  
Feature Fusion ◽  
State Of The Art ◽  
Single Shot ◽  
Multi Scale ◽  
One Stage ◽  
Single Scale ◽  
Feature Pyramid ◽  
Multi Level ◽  
Multiple Levels ◽  
Inference Strategy

Feature pyramids are widely exploited by both the state-of-the-art one-stage object detectors (e.g., DSSD, RetinaNet, RefineDet) and the two-stage object detectors (e.g., Mask RCNN, DetNet) to alleviate the problem arising from scale variation across object instances. Although these object detectors with feature pyramids achieve encouraging results, they have some limitations due to that they only simply construct the feature pyramid according to the inherent multiscale, pyramidal architecture of the backbones which are originally designed for object classification task. Newly, in this work, we present Multi-Level Feature Pyramid Network (MLFPN) to construct more effective feature pyramids for detecting objects of different scales. First, we fuse multi-level features (i.e. multiple layers) extracted by backbone as the base feature. Second, we feed the base feature into a block of alternating joint Thinned U-shape Modules and Feature Fusion Modules and exploit the decoder layers of each Ushape module as the features for detecting objects. Finally, we gather up the decoder layers with equivalent scales (sizes) to construct a feature pyramid for object detection, in which every feature map consists of the layers (features) from multiple levels. To evaluate the effectiveness of the proposed MLFPN, we design and train a powerful end-to-end one-stage object detector we call M2Det by integrating it into the architecture of SSD, and achieve better detection performance than state-of-the-art one-stage detectors. Specifically, on MSCOCO benchmark, M2Det achieves AP of 41.0 at speed of 11.8 FPS with single-scale inference strategy and AP of 44.2 with multi-scale inference strategy, which are the new stateof-the-art results among one-stage detectors. The code will be made available on https://github.com/qijiezhao/M2Det.

scholarly journals FASSD: A Feature Fusion and Spatial Attention-Based Single Shot Detector for Small Object Detection

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1536
Author(s):  
Deng Jiang ◽  
Bei Sun ◽  
Shaojing Su ◽  
Zhen Zuo ◽  
Peng Wu ◽  
...  
Keyword(s):  
Object Detection ◽  
Spatial Attention ◽  
Feature Fusion ◽  
Single Shot ◽  
Small Object ◽  
Feature Maps ◽  
Feature Representations ◽  
Small Object Detection ◽  
High Level ◽  
Detection Speed

Deep learning methods have significantly improved object detection performance, but small object detection remains an extremely difficult and challenging task in computer vision. We propose a feature fusion and spatial attention-based single shot detector (FASSD) for small object detection. We fuse high-level semantic information into shallow layers to generate discriminative feature representations for small objects. To adaptively enhance the expression of small object areas and suppress the feature response of background regions, the spatial attention block learns a self-attention mask to enhance the original feature maps. We also establish a small object dataset (LAKE-BOAT) of a scene with a boat on a lake and tested our algorithm to evaluate its performance. The results show that our FASSD achieves 79.3% mAP (mean average precision) on the PASCAL VOC2007 test with input 300 × 300, which outperforms the original single shot multibox detector (SSD) by 1.6 points, as well as most improved algorithms based on SSD. The corresponding detection speed was 45.3 FPS (frame per second) on the VOC2007 test using a single NVIDIA TITAN RTX GPU. The test results of a simplified FASSD on the LAKE-BOAT dataset indicate that our model achieved an improvement of 3.5% mAP on the baseline network while maintaining a real-time detection speed (64.4 FPS).

Research on workpiece location algorithm based on improved SSD

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lin Li ◽  
Mingheng Fu ◽  
Tie Zhang ◽  
He Ying Wu
Keyword(s):  
Production Efficiency ◽  
Feature Fusion ◽  
Maximum Error ◽  
Industrial Robots ◽  
Single Shot ◽  
Semantic Features ◽  
Location Error ◽  
Content Type ◽  
Center Point ◽  
Location Algorithm

Purpose To improve production efficiency, industrial robots are expected to replace humans to complete the traditional manual operation on grasping, sorting and assembling workpieces. These implementations are closely related to the accuracy of workpiece location. However, workpiece location methods based on conventional machine vision are sensitive to the factors such as light intensity and surface roughness. To enhance the robustness of the workpiece location method and improve the location accuracy, a workpiece location algorithm based on improved Single Shot MultiBox Detector (SSD) is proposed. Design/methodology/approach The proposed algorithm integrates a weighted bi-directional feature pyramid network into SSD. A feature fusion architecture is structured by the combination of low-resolution, strong semantic features and high-resolution, weak semantic features. The architecture is built through a top-down pathway, bottom-up pathway, lateral connections and skip connections. To avoid treating all features equally, learnable weights are introduced into each feature layer to characterize its importance. More detailed information from the low-level layers is injected into the high-level layers, which could improve the accuracy of workpiece location. Findings It is found that the maximum location error at the center point calculated from the proposed algorithm is decreased by more than 22% compared with that of the SSD algorithm. Besides, the average location error evolves a decrease by at least 5%. In the trajectory prediction experiment of the workpiece center point, the results of the proposed algorithm demonstrate that the average location error is below 0.13 mm and the maximum error is no more than 0.23 mm. Originality/value In this work, a workpiece location algorithm based on improved SSD is developed to extract the center point of the workpiece. The results demonstrate that the proposed algorithm is beneficial for workpiece location. The proposed algorithm can be readily used in a variety of workpieces or adapted to other similar tasks.

scholarly journals Special Issue: Advances in Computational Electromagnetics

2021 ◽  
Vol 7 (6) ◽  
pp. 89
Author(s):  
Valerio De Santis
Keyword(s):  
Rare Earth ◽  
Magnetic Materials ◽  
Permanent Magnets ◽  
Computational Electromagnetics ◽  
Superconducting Materials ◽  
Special Issue ◽  
Full Characterization ◽  
Recent Advances

Recent advances in computational electromagnetics (CEMs) have made the full characterization of complex magnetic materials possible, such as superconducting materials, composite or nanomaterials, rare-earth free permanent magnets, etc [...]

scholarly journals Development of Immunohistochemical Methods for Casein Detection in Meat Products

Foods ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 28
Author(s):  
Ludmila Kalčáková ◽  
Matej Pospiech ◽  
Bohuslava Tremlová ◽  
Zdeňka Javůrková ◽  
Irina Chernukha
Keyword(s):  
Milk Protein ◽  
Production Efficiency ◽  
Raw Materials ◽  
Meat Products ◽  
Milk Proteins ◽  
Increase Production Efficiency ◽  
Allergenic Potential ◽  
Significant Difference ◽  
Dairy Ingredients ◽  
Immunohistochemical Methods

To increase production efficiency of meat products, milk protein additives are often used. Despite a number of advantages, use of dairy ingredients involves a certain risk, namely the allergenic potential of milk proteins. A number of methods have been developed to detect milk-origin raw materials in foodstuffs, including immunological reference methods. This study presents newly developed immunohistochemical (IHC) methods for casein detection in meat products. Casein was successfully detected directly in meat products where sensitivity was determined at 1.21 and specificity at 0.28. The results obtained from the IHC were compared with the Enzyme-Linked Immuno Sorbent Assay (ELISA) and there was no statistically significant difference between the IHC and ELISA methods (p > 0.05). The correspondence between the methods was 72% in total. The highest correspondence was reached in frankfurters (90%), the lowest in canned pâté (44%).

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