Study on the detection technology for inner-wall outer surface defects of the automotive ABS brake master cylinder based on BM-YOLOv8

To address the issue of false positive detections in image anomaly detection caused by the loss of low-frequency features when dealing with high-dimensional feature distributions, we propose the Multi-layer Gaussian Discriminant Anomaly Detection Model (MGAD). This model utilizes distance metrics based on multiple normal distributions to perform anomaly detection. By mining multi-layer feature combinations from normal samples and incorporating a Gaussian Mixture Model (GMM) strategy for pixel-by-pixel probability density estimation, a weighting mechanism is designed to emphasize the role of low-frequency features in Gaussian space. This approach effectively models data collections that do not follow a single normal distribution as a mixture of several Gaussian distributions, thereby reducing false detections. Additionally, we propose a method for calculating the minimum Mahalanobis distance based on the estimation of the Minimum Covariance Determinant (MCD). By identifying a subset with the smallest covariance matrix determinant, this method enhances the robust estimation of the data's central position and spread, thereby reducing the impact of outliers. On the MVTec-AD dataset, MGAD demonstrates outstanding performance with an anomaly detection AUROC (Area Under the Receiver Operating Characteristic curve) of 98.8%, the anomaly localization AUROC of 98.2%, and the pTNR (Local True Negative Rate) for normal samples of 93.1%. Compared with the state-of-the-art models, MGAD improves the detection accuracy for normal samples by 3.6%, demonstrating the best performance among all models.These results highlight the model’s excellent capability in anomaly recognition and reduction of false positives.

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LSYOLO: an algorithm for linear scan PCB defect detection

Chen,

2024

Meas. Sci. Technol.

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Aiming at the current problems that different defects in linear scan PCB have scale differences and some defects have high similarity with the background, which are difficult to localize and classify, an expanded receptive field PCB defect detection algorithm is proposed to be applied to the defect detection of linear scan circuit boards. The expanded receptive field module (ERFM) is used in the backbone of YOLOv8 to replace C2f, which can avoid information loss and gridding artifacts while obtaining better contextual information to improve the detection performance of defects with high background similarity. Then, the spatial selective feature pyramid (SSFPN) is used as the FPN to enhance the model's ability to detect defects at different scales while reducing the model performance requirements by utilizing the information of the spatial dimensions of the feature maps to fuse the feature maps at different scales. Wise-IoU is used as the bounding box loss function, and Slide Loss is used as the classification loss function to enhance the model's focus on difficult-to-localize and difficult-to-classify samples. Comparison experiments are conducted on a linear scan printed circuit board dataset, and the experimental results show that the improved model obtains a significant improvement in the detection performance of defects with high detection difficulty; the average precision of the overall defects is improved by 9.6%, the number of model parameters is reduced by 40%, the amount of computation is reduced by 20%, and the size of the model weights file is only 3.64MB. Detecting defects in linear scan PCB is more efficient and lighter than other algorithms.

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Study on the detection technology for inner-wall outer surface defects of the automotive ABS brake master cylinder based on BM-YOLOv8

Cited by 6 publications

References 34 publications

SME: Spatial multi-scale enhanced attention for automated detection of micro-defect on automobile complex paint surfaces

SME: Spatial multi-scale enhanced attention for automated detection of micro-defect on automobile complex paint surfaces

Industrial image anomaly detection based on multi Gaussian discriminant model and robust core set

LSYOLO: an algorithm for linear scan PCB defect detection

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