Detection of Micro-Defects on Metal Screw Surfaces Based on Deep Convolutional Neural Networks

Song, Limei; Li, Xinyao; Yang, Yangang; Zhu, Xinjun; Guo, Qinghua; Yang, Huaidong

doi:10.3390/s18113709

Cited by 43 publications

(28 citation statements)

References 22 publications

(16 reference statements)

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“…The automatic defect detection system will save time and labor costs. Song et al [32] considered the defect detection problem of surface damage, surface dirt, and stripped screws; proposed the screw surface defect detection technology based on the CNN; and proved that deep learning technology was better than the traditional template matching technology. Wei et al [33] used the CNN to classify the defects of a printed circuit board (PCB) and achieved better classification results for a data set containing 1818 collected images.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Tiny Part Defect Detection System in Manufacturing Using Deep Learning

et al. 2019

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We adopted actual intelligent production requirements and proposed a tiny part defect detection method to obtain a stable and accurate real-time tiny part defect detection system and solve the problems of manually setting conveyor speed and industrial camera parameters in defect detection for factory products. First, we considered the important influences of the properties of tiny parts and the environmental parameters of a defect detection system on its stability. Second, we established a correlation model between the detection capability coefficient of the part system and the moving speed of the conveyor. Third, we proposed a defect detection algorithm for tiny parts that are based on a single short detector network (SSD) and deep learning. Finally, we combined an industrial real-time detection platform with the missed detection algorithm for mechanical parts based on intermediate variables to address the problem of missed detections. We used a 0.8 cm darning needle as the experimental object. The system defect detection accuracy was the highest when the speed of the conveyor belt was 7.67 m/min.

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Section: Related Workmentioning

confidence: 99%

Real-Time Tiny Part Defect Detection System in Manufacturing Using Deep Learning

et al. 2019

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“…At present, most of the detection objects are still plane-oriented. For example, Yi, et al [17] proposed the detection of surface defects of end-to-end steel strips based on deep convolutional neural networks; Ma, et al [18] proposed blister defect detection based on convolutional neural networks for polymer lithium-ion batteries; He, et al [19] proposed a new object detection framework classification priority network (CPN) and a new classification network multi-group convolutional neural network (MG-CNN) to detect steel surface defects, using the You Only Look Once: Unified, Real-Time Object Detection (YOLO) neural network-the accuracy rate of hot-rolled strip surface defects could reach more than 94% and the classification rate is above 96%; Liu, et al [20] proposed periodic surface defect detection of steel plates based on deep learning to improve the detection rate by improving the Long Short-Term Memory (LSTM) network; and Song, et al [21] proposed a DCNN (deep convolutional neural network) to detect the micro-defects of metal screw end faces, and the detection accuracy could reach 98%. The image detection obtained by directly taking photos of such a plane is mature and the detection accuracy is generally good.…”

Section: Introductionmentioning

confidence: 99%

Object Detection and Classification of Metal Polishing Shaft Surface Defects Based on Convolutional Neural Network Deep Learning

Qingsheng

Tan

et al. 2019

Applied Sciences

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Defective shafts need to be classified because some defective shafts can be reworked to avoid replacement costs. Therefore, the detection and classification of shaft surface defects has important engineering application value. However, in the factory, shaft surface defect inspection and classification are done manually, with low efficiency and reliability. In this paper, a deep learning method based on convolutional neural network feature extraction is used to realize the object detection and classification of metal shaft surface defects. Through image segmentation, the system methods setting of a Fast-R-CNN object detection framework and parameter optimization settings are implemented to realize the classification of 16,384 × 4096 large image little objects. The experiment proves that the method can be applied in practical production and can also be extended to other fields of large image micro-fine defects with a high light surface. In addition, this paper proposes a method to increase the proportion of positive samples by multiple settings of IOU values and discusses the limitations of the system for defect detection.

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“…This behavior allows convolutional neural networks to learn the features from the data, which is the reason they perform well for image recognition tasks. Hence, such convolutional neural networks are used in various application fields for defect detection [5,6,7,8,9,10,11,12]. More information regarding deep neural networks can be found in [13].…”

Section: Introductionmentioning

confidence: 99%

Classifying Image Stacks of Specular Silicon Wafer Back Surface Regions: Performance Comparison of CNNs and SVMs

Kofler

Muhr

Spöck

2019

Sensors

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In this work, we compare the performance of convolutional neural networks and support vector machines for classifying image stacks of specular silicon wafer back surfaces. In these image stacks, we can identify structures typically originating from replicas of chip structures or from grinding artifacts such as comets or grinding grooves. However, defects like star cracks are also visible in those images. To classify these image stacks, we test and compare three different approaches. In the first approach, we train a convolutional neural net performing feature extraction and classification. In the second approach, we manually extract features of the images and use these features to train support vector machines. In the third approach, we skip the classification layers of the convolutional neural networks and use features extracted from different network layers to train support vector machines. Comparing these three approaches shows that all yield an accuracy value above 90%. With a quadratic support vector machine trained on features extracted from a convolutional network layer we achieve the best compromise between precision and recall rate of the class star crack with 99.3% and 98.6%, respectively.

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Detection of Micro-Defects on Metal Screw Surfaces Based on Deep Convolutional Neural Networks

Cited by 43 publications

References 22 publications

Real-Time Tiny Part Defect Detection System in Manufacturing Using Deep Learning

Real-Time Tiny Part Defect Detection System in Manufacturing Using Deep Learning

Object Detection and Classification of Metal Polishing Shaft Surface Defects Based on Convolutional Neural Network Deep Learning

Classifying Image Stacks of Specular Silicon Wafer Back Surface Regions: Performance Comparison of CNNs and SVMs

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