Anomaly detection in periodic motion scenes based on multi-scale feature Gaussian weighting analysis

Wang, Qi; Meng, Fanwu; Huang, Zhipeng; Li, Kejing

doi:10.1088/1361-6501/ab0479

Cited by 6 publications

(5 citation statements)

References 28 publications

(29 reference statements)

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“…where: h (x) is the filtered grey value of point x; c is the weight of Gaussian space; s is the Gaussian weight of the degree of similarity between pixels [25]; f (x) is the pixel value of the pixel point; k −1 (x) is the normalization factor.…”

Section: A Adaptive Canny Detection 1) Image Pre-processingmentioning

confidence: 99%

Adaptive Canny and Semantic Segmentation Networks Based on Feature Fusion for Road Crack Detection

et al. 2023

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Road cracks, which are a common hazard in pavements throughout the life cycle of a road, can degrade the performance of the road, shorten its service life, and endanger the safety of vehicles. Traditional vision machine detection methods can detect road crack details but suffer from poor stability and generalization ability, whereas semantic segmentation detection, although more stable, cannot track fine road crack information. To combine the advantages of both methods and improve the accuracy of road crack detection, a novel feature fusion road crack detection method is proposed in this study. First, the bilateral filter and four-way Sobel operator are introduced into the Canny algorithm to enhance the noise reduction effect and extract edge features more effectively. Second, the dynamic threshold is generated adaptively by the gradient information after non-maximum suppression. Subsequently, the detection map is morphologically processed, the connected areas are ranked, and the bilateral filter parameters are adjusted based on the detection results. The Canny road crack detection map is then extracted by the convolutional feature extraction module, fused with the low feature layer in the DeepLabV3+ detection network, and finally stitched with the high feature layer; the resulting map is obtained after convolutional feature extraction. The method was validated on the publicly available complex road crack dataset CRACK500; the experimental results showed that feature fusion outperformed the adaptive Canny, DeepLabV3+, Unet, PSPnet, and ICnet algorithms by more than 6.5% on the Mean Intersection over Union(MIoU) and also in Mean Absolute Error(MAE) by effectively combining crack features and improving the detection accuracy.

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Section: A Adaptive Canny Detection 1) Image Pre-processingmentioning

confidence: 99%

Adaptive Canny and Semantic Segmentation Networks Based on Feature Fusion for Road Crack Detection

et al. 2023

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“…The first stage of image segmentation involves building the standard template image library by decomposing the periodic motion scene of the stamping production line, using the method proposed by Wang et al [52].…”

Section: Image Segmentationmentioning

confidence: 99%

“…In practice, shooting can only occur from the side of the stamping parts. The geometric modeling [52] of the experimental scene is shown in Figure 4. It is assumed that the target's movement speed is V, and the direction of speed is shown in the figure . The camera shoots at a downward angle of β degrees, and L is the distance from the lens to the target point.…”

Section: Image Segmentationmentioning

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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“…Video abnormal event detection technology is extensively applied in industry, security, and traffic sectors for early fault warning. Its purpose is to spatially and temporally locate abnormal events within surveillance videos [1][2][3][4][5][6][7]. A formal definition of video anomalies is 'the occurrence of unusual appearance or motion attributes or the occurrence of usual appearance or motion attributes in unusual locations or times [8]'.…”

Section: Introductionmentioning

confidence: 99%

Anomaly detection method based on temporal spatial information enhancement

Wang,

et al. 2023

Meas. Sci. Technol.

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The proposed Anomaly Detection Method Based on Temporal Spatial Information Enhancement addresses the limitations of unsupervised techniques in detecting abnormal events beyond boundaries and limited samples. It incorporates a Serial Depth Separable Residual Block (Serial Block) as the backbone for predicting future frame. Additionally, a DenseReserve Subsample Module (DRSM) facilitates feature scale scaling, and a U-shaped Pyramid Attention Module (UPAM) guides feature fusion and enhances spatial details. During the prediction stage, reconstructed optical flow information aids in distinguishing abnormal and normal event features, with the abnormal score determined by a weighted fusion of optical flow reconstruction error and prediction error for future frames. Experimental results demonstrate the method’s outstanding performance, achieving area under the curve metrics of 99.7%, 92.1%, and 78.3% on UCSD Ped2, CUHK Avenue, and ShahanghaiTech datasets, respectively. This method offers significant advancements in detecting anomalous events in complex and multi-scene surveillance videos, with improved operational speed and reduced parameters.

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Anomaly detection in periodic motion scenes based on multi-scale feature Gaussian weighting analysis

Cited by 6 publications

References 28 publications

Adaptive Canny and Semantic Segmentation Networks Based on Feature Fusion for Road Crack Detection

Adaptive Canny and Semantic Segmentation Networks Based on Feature Fusion for Road Crack Detection

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

Anomaly detection method based on temporal spatial information enhancement

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