Defect detection of nickel plated punched steel strip based on improved least square method

Cao, Baoxiang; Li, Jianqi; Liu, Chengfa; Qin, Lingjie

doi:10.1016/j.ijleo.2020.164331

Cited by 10 publications

(4 citation statements)

References 11 publications

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“…Scheme 2: The system uses the STM32F103 processor. STM32 series runs fast, clock frequency up to 72MHz, 11 timers and rich IO ports, and built-in three 12-bit A / D converters, two 12-bit D / A converters, its advanced timer can produce PWM waves with dead zone complementary, cost-effective [2] .…”

Section: The Selection Of System Design 11 the Argument And Selection...mentioning

confidence: 99%

The design of paper counting device based on STM32

Li¹,

xie²

2022

5th International Conference on Computer Information Science and Application Technology (CISAT 2022)

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Section: The Selection Of System Design 11 the Argument And Selection...mentioning

confidence: 99%

The design of paper counting device based on STM32

Li¹,

xie²

2022

5th International Conference on Computer Information Science and Application Technology (CISAT 2022)

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“…So far, few vision-based methods have been proposed for online stamping process monitoring. The vision-based detection techniques related to stamping progressive dies mainly focus on offline workpiece quality monitoring, such as threshold-based methods [21,22], edge-based methods [23], and template matching-based methods [24], and these methods were compared in [25]. Stamping workpiece quality monitoring methods are mainly used for offline defect detection.…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection in the Production Process of Stamping Progressive Dies Using the Shape- and Size-Adaptive Descriptors

Ma,

Meng

2023

Sensors

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In the production process of progressive die stamping, anomaly detection is essential for ensuring the safety of expensive dies and the continuous stability of the production process. Early monitoring processes involve manually inspecting the quality of post-production products to infer whether there are anomalies in the production process, or using some sensors to monitor some state signals during the production process. However, the former is an extremely tedious and time-consuming task, and the latter cannot provide warnings before anomalies occur. Both methods can only detect anomalies after they have occurred, which usually means that damage to the die has already been caused. In this paper, we propose a machine-vision-based method for real-time anomaly detection in the production of progressive die stamping. This method can detect anomalies before they cause actual damage to the mold, thereby stopping the machine and protecting the mold and machine. In the proposed method, a whole continuous motion scene cycle is decomposed into a standard background template library, and the potential anomaly regions in the image to be detected are determined according to the difference from the background template library. Finally, the shape- and size-adaptive descriptors of these regions and corresponding reference regions are extracted and compared to determine the actual anomaly regions. The experimental results indicate that this method can achieve reasonable accuracy in the detection of anomalies in the production process of stamping progressive dies. The experimental results demonstrate that this method not only achieves satisfactory accuracy in anomaly detection during the production of progressive die stamping, but also attains competitive performance levels when compared with methods based on deep learning. Furthermore, it requires simpler preliminary preparations and does not necessitate the adoption of the deep learning paradigm.

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“…Hao et al [4] proposed a new adaptive Canny algorithm without manually setting the parameters. Cao et al [5] built a machine vision detection system using an improved least-squares method. Hao et al [6] proposed a method to enhance the signal using improved Shannon entropy to reduce the noise generated by the track at high speeds.…”

Section: Introductionmentioning

confidence: 99%

“…(1) For the SOD of no-service rail surface detection, we propose a supervised deep learning network named TSSTNet and innovatively introduce the transformer as the backbone of the network; (2) A two-stream encoder and a three-stream decoder are proposed to eliminate the adverse effects between tasks; (3) A contour alignment module is presented to connect multitasking and reduce the noise at the edges of the saliency maps; (4) A multi-feature fusion module is proposed to converge the feature maps in the three different streams of the decoder; (5) We conducted a comparison experiment on the NRSD-MN [2] dataset with 10 SOTA methods [13,14,[16][17][18][19][20][21][22][23]. The results indicate that our network performs better than the other networks on five metrics.…”

Section: Introductionmentioning

confidence: 99%

TSSTNet: A Two-Stream Swin Transformer Network for Salient Object Detection of No-Service Rail Surface Defects

Wan

Song

2022

Coatings

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The detection of no-service rail surface defects is important in the rail manufacturing process. Detection of defects can prevent significant financial losses. However, the texture and form of the defects are often very similar to the background, which makes them difficult for the human eye to distinguish. How to accurately identify rail surface defects thus poses a challenge. We introduce salient object detection through machine vision to deal with this challenge. Salient object detection locates the most “significant” areas of an image using algorithms, which constitute an integral part of machine vision inspection. However, existing saliency detection networks suffer from inaccurate positioning, poor contouring, and incomplete detection. Therefore, we propose an innovative deep learning network named Two-Stream Swin Transformer Network (TSSTNet) for salient detection of no-service rail surface defects. Specifically, we propose a two-stream encoder—one stream for feature extraction and the other for edge extraction. TSSTNet also includes a three-stream decoder, consisting of a saliency stream, edge stream, and fusion stream. For the problem of incomplete detection, we innovatively introduce the Swin Transformer to model global information. For the problem of unclear contours, we expect to deepen the understanding of the difference in depth between the foreground and background through the learning of contour maps, so the contour alignment module (CAM) is created to deal with this problem. Moreover, to make the most of multimodal information, we suggest a multi-feature fusion module (MFFM). Finally, we conducted comparative experiments with 10 state-of-the-art (SOTA) approaches on the NRSD-MN datasets, and our model performed more competitively than others on five metrics.

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Defect detection of nickel plated punched steel strip based on improved least square method

Cited by 10 publications

References 11 publications

The design of paper counting device based on STM32

The design of paper counting device based on STM32

Anomaly Detection in the Production Process of Stamping Progressive Dies Using the Shape- and Size-Adaptive Descriptors

TSSTNet: A Two-Stream Swin Transformer Network for Salient Object Detection of No-Service Rail Surface Defects

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