Wafer Surface Defect Detection Based on Background Subtraction and Faster R-CNN

Zheng, Jiebing; Zhang, Tao

doi:10.3390/mi14050905

Cited by 6 publications

(2 citation statements)

References 26 publications

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“…This increases the accuracy in fabric defect detection. Zheng et al [17] focused on the problem that wafer surface defects are easily confused with the background and are difficult to detect. A new method for wafer surface defect detection based on background subtraction and Faster R-CNN is proposed.…”

Section: Related Workmentioning

confidence: 99%

Study on the Sustainable Detection of Machining Surface Defects under the Influence of Environmental Interference

et al. 2023

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When detecting surface defects in the industrial cutting environment, the defects are easily contaminated and covered by many interference factors (such as chips and coolant residue) that exist on the machined surface. These interfering factors hinder the sustainable detection of surface defects. Furthermore, addressing the challenge of detecting surface defects in the presence of interference factors has proven to be a difficult problem in the current detection field. To solve this problem, a sustainable detection method for surface defects is proposed. The method is divided into two steps: one is the identification and removal of interference factors; the other is the detection of surface defects. First, a new FPN-DepResUnet model is constructed by modifying the Unet model from three aspects. The FPN-DepResUnet model is used to identify the interference factors in the image. Compared to the Unet model, the MAP of the FPN-DepResUnet model is increased by 5.77%, reaching 94.82%. The interfering factors are then removed using the RFR-net model. The RFR-net model performs point-to-point repair of interference regions. The repair process is performed by finding high-quality pixels similar to the interference region from the rest of the image. The negative effects of the interfering factors are removed by combining the FPN-DepResUnet model with the RFR-net model. On this basis, the SAM-Mask RCNN model is proposed for efficient defect detection of clean surface images. Compared with the Mask RCNN model, the MAP of the proposed SAM-Mask RCNN model increased by 2.00%, reaching 94.62%. Further, the inspection results can be fed back with a variety of surface defect information including defect types, the number of pixels in the different defect regions, and the proportion of different defect regions in the entire image. This enables predictive maintenance and control of the machined surface quality during machining.

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Section: Related Workmentioning

confidence: 99%

Study on the Sustainable Detection of Machining Surface Defects under the Influence of Environmental Interference

et al. 2023

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“…Yan [4] proposed a PCB defect detection method FCM-YOLO based on feature enhancement and multi-scale fusion, which is based on YOLOv5s, and introduces a combination of spatialto-depth layers and non-spanning convolutional layers in the feature extraction network to construct a feature re-extraction module to reduce information loss and retain small target feature information thus improving detection capability. Geng [5] proposed a PCB surface defect detection algorithm based on the Faster-R CNN algorithm, which introduces a region generating network instead of a non-selective algorithm to generate candidate regions to improve the detection and identification of several more typical surface defects. However, the industrial detection should satisfy the accuracy and at the same time ensure the speed of detection, the above methods have the problems of large network structure, complex model, and insufficient detection accuracy.…”

Section: Introductionmentioning

confidence: 99%

Improved Printed Circuit Board Defect Detection Scheme

Bai,

2024

Preprint

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In this paper, an improved printed circuit board(PCB)defect detection scheme named PD-YOLOv8 is proposed, which is specialized in the common and challenging problem of small target recognition in PCB inspection. This improved scheme mainly relies on the basic framework of YOLOv8n, and effectively enhances the detection performance of PCB small defects through multiple innovative designs. First, we incorporate the E-CANet attention mechanism into the backbone network of YOLOv8, which improves the performance of small object detection by adaptively enhancing the expressiveness of key features, so that the network possesses higher sensitivity and focus on tiny details in PCB images. Second, we optimize and upgraded the neck structure. On the one hand, the C2f E module is introduced to facilitate cross-layer feature fusion to ensure that the rich texture information at the lower layer and the abstract semantic information at the higher layer complement each other, which is conducive to improving the contextual understanding of small object detection; on the other hand, a detection head specialized for small targets is designed and added to enhance the ability of locating and identifying tiny defects. Furthermore, in order to further enhance the interaction and fusion of multi-scale features, we also add a SlimNeck module to the neck structure, which realizes efficient information transfer through streamlined design and reduces computational complexity at the same time. In addition, we draw on the advanced BiFPN structure, which enables the bidirectional flow of feature information between multiple layers and greatly improves the capture and integration of small target features. Compared to the original YOLOv8 algorithm , this algorithm improves the average accuracy on small targets by 5.7% for mAP50 and 2.2% for mAP50-95.

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