Computer Vision and Normalizing Flow Based Defect Detection

• URL: http://arxiv.org/abs/2012.06737v1
• Date: Sat, 12 Dec 2020 05:38:21 GMT
• Title: Computer Vision and Normalizing Flow Based Defect Detection
• Authors: Zijian Kuang and Xinran Tie
• Abstract summary: We present a two-stage defect detection network based on the object detection model YOLO, and the normalizing flow-based defect detection model DifferNet. Our model has high robustness and performance on defect detection using real-world video clips taken from a production line monitoring system. Our proposed model can learn on a small number of defect-free samples of single or multiple product types.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Surface defect detection is essential and necessary for controlling the qualities of the products during manufacturing. The challenges in this complex task include: 1) collecting defective samples and manually labeling for training is time-consuming; 2) the defects' characteristics are difficult to define as new types of defect can happen all the time; 3) and the real-world product images contain lots of background noise. In this paper, we present a two-stage defect detection network based on the object detection model YOLO, and the normalizing flow-based defect detection model DifferNet. Our model has high robustness and performance on defect detection using real-world video clips taken from a production line monitoring system. The normalizing flow-based anomaly detection model only requires a small number of good samples for training and then perform defect detection on the product images detected by YOLO. The model we invent employs two novel strategies: 1) a two-stage network using YOLO and a normalizing flow-based model to perform product defect detection, 2) multi-scale image transformations are implemented to solve the issue product image cropped by YOLO includes many background noise. Besides, extensive experiments are conducted on a new dataset collected from the real-world factory production line. We demonstrate that our proposed model can learn on a small number of defect-free samples of single or multiple product types. The dataset will also be made public to encourage further studies and research in surface defect detection.

Related papers

• Fine-grained Abnormality Prompt Learning for Zero-shot Anomaly Detection [88.34095233600719]
  FAPrompt is a novel framework designed to learn Fine-grained Abnormality Prompts for more accurate ZSAD. It substantially outperforms state-of-the-art methods by at least 3%-5% AUC/AP in both image- and pixel-level ZSAD tasks.
  arXiv  Detail & Related papers  (2024-10-14T08:41:31Z)
• Robust Tiny Object Detection in Aerial Images amidst Label Noise [50.257696872021164]
  This study addresses the issue of tiny object detection under noisy label supervision. We propose a DeNoising Tiny Object Detector (DN-TOD), which incorporates a Class-aware Label Correction scheme. Our method can be seamlessly integrated into both one-stage and two-stage object detection pipelines.
  arXiv  Detail & Related papers  (2024-01-16T02:14:33Z)
• Self-supervised Feature Adaptation for 3D Industrial Anomaly Detection [59.41026558455904]
  We focus on multi-modal anomaly detection. Specifically, we investigate early multi-modal approaches that attempted to utilize models pre-trained on large-scale visual datasets. We propose a Local-to-global Self-supervised Feature Adaptation (LSFA) method to finetune the adaptors and learn task-oriented representation toward anomaly detection.
  arXiv  Detail & Related papers  (2024-01-06T07:30:41Z)
• A Novel Strategy for Improving Robustness in Computer Vision Manufacturing Defect Detection [1.3198689566654107]
  Visual quality inspection in high performance manufacturing can benefit from automation, due to cost savings and improved rigor. Deep learning techniques are the current state of the art for generic computer vision tasks like classification and object detection. Manufacturing data can pose a challenge for deep learning because data is highly repetitive and there are few images of defects or deviations to learn from.
  arXiv  Detail & Related papers  (2023-05-16T12:51:51Z)
• Few-Shot Defect Image Generation via Defect-Aware Feature Manipulation [19.018561017953957]
  We propose the first defect image generation method in the challenging few-shot cases. Our method consists of two training stages. First, we train a data-efficient StyleGAN2 on defect-free images as the backbone. Second, we attach defect-aware residual blocks to the backbone, which learn to produce reasonable defect masks.
  arXiv  Detail & Related papers  (2023-03-04T11:43:08Z)
• Recognition of Defective Mineral Wool Using Pruned ResNet Models [88.24021148516319]
  We developed a visual quality control system for mineral wool. X-ray images of wool specimens were collected to create a training set of defective and non-defective samples. We obtained a model with more than 98% accuracy, which in comparison to the current procedure used at the company, it can recognize 20% more defective products.
  arXiv  Detail & Related papers  (2022-11-01T13:58:02Z)
• An Outlier Exposure Approach to Improve Visual Anomaly Detection Performance for Mobile Robots [76.36017224414523]
  We consider the problem of building visual anomaly detection systems for mobile robots. Standard anomaly detection models are trained using large datasets composed only of non-anomalous data. We tackle the problem of exploiting these data to improve the performance of a Real-NVP anomaly detection model.
  arXiv  Detail & Related papers  (2022-09-20T15:18:13Z)
• Reference-based Defect Detection Network [57.89399576743665]
  The first issue is the texture shift which means a trained defect detector model will be easily affected by unseen texture. The second issue is partial visual confusion which indicates that a partial defect box is visually similar with a complete box. We propose a Reference-based Defect Detection Network (RDDN) to tackle these two problems.
  arXiv  Detail & Related papers  (2021-08-10T05:44:23Z)
• Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows [24.734388664558708]
  We propose DifferNet: It leverages the descriptiveness of features extracted by convolutional neural networks to estimate their density. Based on these likelihoods we develop a scoring function that indicates defects. We demonstrate the superior performance over existing approaches on the challenging and newly proposed MVTec AD and Magnetic Tile Defects datasets.
  arXiv  Detail & Related papers  (2020-08-28T10:49:28Z)
• Few-Shot Defect Segmentation Leveraging Abundant Normal Training Samples Through Normal Background Regularization and Crop-and-Paste Operation [4.626338154327536]
  In industrial inspection tasks, it is common to capture abundant defect-free image samples but very limited anomalous ones. This paper tackles the challenging few-shot defect segmentation task with sufficient normal (defect-free) training images but very few anomalous ones. We present two effective regularization techniques via incorporating abundant defect-free images into the training of a UNet-like encoder-decoder defect segmentation network.
  arXiv  Detail & Related papers  (2020-07-18T14:15:42Z)
• Semi-supervised Anomaly Detection using AutoEncoders [0.0]
  Anomaly detection refers to the task of finding unusual instances that stand out from the normal data. In this paper, we present a convolutional auto-encoder architecture for anomaly detection that is trained only on the defect-free (normal) instances. The approach was tested on two data-sets and achieved an impressive average F1 score of 0.885.
  arXiv  Detail & Related papers  (2020-01-06T23:06:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.