Evaluation of Data Augmentation and Loss Functions in Semantic Image Segmentation for Drilling Tool Wear Detection

• URL: http://arxiv.org/abs/2302.05262v2
• Date: Fri, 9 Feb 2024 13:40:22 GMT
• Title: Evaluation of Data Augmentation and Loss Functions in Semantic Image Segmentation for Drilling Tool Wear Detection
• Authors: Elke Schlager, Andreas Windisch, Lukas Hanna, Thomas Kl\"unsner, Elias Jan Hagendorfer, Tamara Teppernegg
• Abstract summary: We present a U-Net based semantic image segmentation pipeline, deployed on microscopy images of cutting inserts. The wear area is differentiated in two different types, resulting in a multiclass classification problem. We find, that the best performing models are binary models, trained on data with moderate augmentation and an IoU-based loss function.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Tool wear monitoring is crucial for quality control and cost reduction in manufacturing processes, of which drilling applications are one example. In this paper, we present a U-Net based semantic image segmentation pipeline, deployed on microscopy images of cutting inserts, for the purpose of wear detection. The wear area is differentiated in two different types, resulting in a multiclass classification problem. Joining the two wear types in one general wear class, on the other hand, allows the problem to be formulated as a binary classification task. Apart from the comparison of the binary and multiclass problem, also different loss functions, i. e., Cross Entropy, Focal Cross Entropy, and a loss based on the Intersection over Union (IoU), are investigated. Furthermore, models are trained on image tiles of different sizes, and augmentation techniques of varying intensities are deployed. We find, that the best performing models are binary models, trained on data with moderate augmentation and an IoU-based loss function.

Related papers

• Visual Context-Aware Person Fall Detection [52.49277799455569]
  We present a segmentation pipeline to semi-automatically separate individuals and objects in images. Background objects such as beds, chairs, or wheelchairs can challenge fall detection systems, leading to false positive alarms. We demonstrate that object-specific contextual transformations during training effectively mitigate this challenge.
  arXiv  Detail & Related papers  (2024-04-11T19:06:36Z)
• Toward Multi-class Anomaly Detection: Exploring Class-aware Unified Model against Inter-class Interference [67.36605226797887]
  We introduce a Multi-class Implicit Neural representation Transformer for unified Anomaly Detection (MINT-AD) By learning the multi-class distributions, the model generates class-aware query embeddings for the transformer decoder. MINT-AD can project category and position information into a feature embedding space, further supervised by classification and prior probability loss functions.
  arXiv  Detail & Related papers  (2024-03-21T08:08:31Z)
• Multi-task Learning for Optical Coherence Tomography Angiography (OCTA) Vessel Segmentation [1.7539061565898157]
  We propose a novel multi-task learning method for OCTA segmentation, called OCTA-MTL. The adaptive loss combination strategy dynamically adjusts the loss weights according to the inverse of the average loss values of each task. We evaluate our method on the ROSE-2 dataset its superiority in terms of segmentation performance against two baseline methods.
  arXiv  Detail & Related papers  (2023-11-03T23:10:56Z)
• Investigating Shift Equivalence of Convolutional Neural Networks in Industrial Defect Segmentation [3.843350895842836]
  In industrial defect segmentation tasks, output consistency (also referred to equivalence) of the model is often overlooked. A novel pair of down/upsampling layers called component attention polyphase sampling (CAPS) is proposed as a replacement for the conventional sampling layers in CNNs. The experimental results on the micro surface defect (MSD) dataset and four real-world industrial defect datasets demonstrate that the proposed method exhibits higher equivalence and segmentation performance.
  arXiv  Detail & Related papers  (2023-09-29T00:04:47Z)
• Joint localization and classification of breast tumors on ultrasound images using a novel auxiliary attention-based framework [7.6620616780444974]
  We propose a novel joint localization and classification model based on the attention mechanism and disentangled semi-supervised learning strategy. The proposed modularized framework allows flexible network replacement to be generalized for various applications.
  arXiv  Detail & Related papers  (2022-10-11T20:14:13Z)
• Treatment Learning Causal Transformer for Noisy Image Classification [62.639851972495094]
  In this work, we incorporate this binary information of "existence of noise" as treatment into image classification tasks to improve prediction accuracy. Motivated from causal variational inference, we propose a transformer-based architecture, that uses a latent generative model to estimate robust feature representations for noise image classification. We also create new noisy image datasets incorporating a wide range of noise factors for performance benchmarking.
  arXiv  Detail & Related papers  (2022-03-29T13:07:53Z)
• Unsupervised Domain-Specific Deblurring using Scale-Specific Attention [0.25797036386508543]
  We propose unsupervised domain-specific deblurring using a scale-adaptive attention module (SAAM) Our network does not require supervised pairs for training, and the deblurring mechanism is primarily guided by adversarial loss. Different ablation studies show that our coarse-to-fine mechanism outperforms end-to-end unsupervised models and SAAM is able to attend better compared to attention models used in literature.
  arXiv  Detail & Related papers  (2021-12-12T07:47:45Z)
• Activation to Saliency: Forming High-Quality Labels for Unsupervised Salient Object Detection [54.92703325989853]
  We propose a two-stage Activation-to-Saliency (A2S) framework that effectively generates high-quality saliency cues. No human annotations are involved in our framework during the whole training process. Our framework reports significant performance compared with existing USOD methods.
  arXiv  Detail & Related papers  (2021-12-07T11:54:06Z)
• A Compositional Feature Embedding and Similarity Metric for Ultra-Fine-Grained Visual Categorization [16.843126268445726]
  Fine-grained visual categorization (FGVC) aims at classifying objects with small inter-class variances. This paper proposes a novel compositional feature embedding and similarity metric ( CECS) for ultra-fine-grained visual categorization. Experimental results on two ultra-FGVC datasets and one FGVC dataset with recent benchmark methods consistently demonstrate that the proposed CECS method achieves the state-the-art performance.
  arXiv  Detail & Related papers  (2021-09-25T15:05:25Z)
• Enhancing Fine-Grained Classification for Low Resolution Images [97.82441158440527]
  Low resolution images suffer from the inherent challenge of limited information content and the absence of fine details useful for sub-category classification. This research proposes a novel attribute-assisted loss, which utilizes ancillary information to learn discriminative features for classification. The proposed loss function enables a model to learn class-specific discriminative features, while incorporating attribute-level separability.
  arXiv  Detail & Related papers  (2021-05-01T13:19:02Z)
• One-Shot Object Detection without Fine-Tuning [62.39210447209698]
  We introduce a two-stage model consisting of a first stage Matching-FCOS network and a second stage Structure-Aware Relation Module. We also propose novel training strategies that effectively improve detection performance. Our method exceeds the state-of-the-art one-shot performance consistently on multiple datasets.
  arXiv  Detail & Related papers  (2020-05-08T01:59:23Z)

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.