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Surface Defects Detection of Cylindrical High-Precision Industrial Parts Based on Deep Learning Algorithms: A Review

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Operations Research Forum 2024年第3期5卷 58页

作者： Wei, Li Solihin, Mahmud Iwan Saruchi, Sarah ‘Atifah Astuti, Winda Hong, Lim Wei Kit, Ang Chun School of Computer Science and Technology (School of Software) Guangxi University of Science and Technology Guangxi 545006 China Faculty of Engineering Technology and Built Environment UCSI University Kuala Lumpur 56000 Malaysia Faculty of Manufacturing and Mechatronics Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah Pahang Pekan 26600 Malaysia Computer Engineering Department Automotive and Robotics Engineering Program BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia

High-precision cylindrical parts are critical components across various industries including aerospace, automotive, and manufacturing. Since these parts play a pivotal role in the performance and safety of the systems they are integrated into, they are often subject to stringent quality control measures. Defects on the interior and exterior wall surfaces of these cylindrical parts can severely undermine their function, leading to degraded performance, increased wear, and even catastrophic failures in extreme cases. This article aims to comprehensively summarize the task definition, challenges, mainstream methods, public datasets, evaluation metrics, and other aspects of surface defect detection for high-precision cylindrical parts, in order to help researchers quickly grasp this field. Specifically, the background and characteristics of industrial defect detection are first introduced. Owing to the unique geometric features of cylindrical part surfaces, algorithms and equipment for image data acquisition used in surface defect detection are elaborated in detail. This article presents an extensive overview of state-of-the-art surface defect detection techniques designed for high-precision cylindrical components, all rooted in deep learning. The methods are systematically classified into three main categories: fully supervised, unsupervised, and alternative approaches, based on their data labeling strategies. Additionally, the paper conducts a comprehensive analysis within each category, shedding light on their unique strengths, limitations, and practical use cases. Concluding the discussion, the paper provides insights into future development trends and potential research directions in this field that will lead to manufacturing innovation. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Anomaly detection computer vision Deep learning Defect detection High-precision cylindrical parts Image processing Optical illumination

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Generalizing 6-DoF Grasp Detection via Domain Prior Knowledge

Generalizing 6-DoF Grasp Detection via Domain Prior Knowledg...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Haoxiang Ma Modi Shi Boyang Gao Di Huang State Key Laboratory of Software Development Environment Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China School of Computer Science and Technology Harbin Institute of Technology Harbin China Geometry Robotics

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We focus on the generalization ability of the 6-DoF grasp detection method in this paper. While learning-based grasp detection methods can predict grasp poses for unseen ob-jects using the grasp distribution learned from the training set, they often exhibit a significant performance drop when encountering objects with diverse shapes and struc-tures. To enhance the grasp detection methods' general-ization ability, we incorporate domain prior knowledge of robotic grasping, enabling better adaptation to objects with significant shape and structure differences. More specifi-cally, we employ the physical constraint regularization during the training phase to guide the model towards predicting grasps that comply with the physical rule on grasping. For the unstable grasp poses predicted on novel objects, we design a contact-score joint optimization using the pro-jection contact map to refine these poses in cluttered sce-narios. Extensive experiments conducted on the GraspNet-1 billion benchmark demonstrate a substantial performance gain on the novel object set and the real-world grasping experiments also demonstrate the effectiveness of our gen-eralizing 6-DoF grasp detection method. Code is available at https://***/mahaoxiang822/Generalizing-Grasp.

关键词： Training computer vision Shape Grasping Benchmark testing Predictive models Performance gain

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A study on the noise reduction method in turbid water environments using Spatial frequency characteristics 9

A study on the noise reduction method in turbid water enviro...

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9th International Conference on Intelligent Informatics and BioMedical sciences, ICIIBMS 2024

作者： Koura, Ayumu Kim, Hyun-Woo Cho, Myungjin Lee, Min-Chul Kyushu Institute of Technology Graduate School of Computer Science and Systems Engineering Iizuka city Japan Hankyong National University School of ICT Robotics and Mechanical Engineering Iitc Anseong-si Korea Republic of

ISBN: (纸本)9798350363043

Scattering noise reduction is a challenging project to remove noise under scattering media conditions such as fog or turbid water. In previous study, blurring caused by scattering medium particles in fog or turbid water was removed by low-pass filtering. However, in this method, the accurate color information of the object may be lost due to rough low-frequency filtering. In addition, it is difficult to the obtain object shape information in heavy turbid environments. To solve this problem, we propose a new de-scattering algorithm that combines Fourier-domain filtering and integral imaging by creating a filter tailored to the frequency to be removed. To prove the proposed method, we perform a numerical analysis of experimental results using conventional and proposed methods. © 2024 IEEE.

关键词： Noise abatement

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A study on Image Processing Method for Noise Reduction in Digital Holographic Microscopy 9

A study on Image Processing Method for Noise Reduction in Di...

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9th International Conference on Intelligent Informatics and BioMedical sciences, ICIIBMS 2024

作者： Nagashima, Masaharu Kim, Hyun-Woo Cho, Myungjin Lee, Min-Chul Kyushu Institute of Technology Graduate School of Computer Science and Systems Engineering Iizuka city Japan Iitc Hankyong National University School of Ict Robotics and Mechanical Engineering Anseong-si Korea Republic of

ISBN: (纸本)9798350363043

Digital Holographic Microscopy (DHM) can obtain three-dimensional (3D) information about the fine structure of an object by utilizing the phase information of coherent light. In DHM, during the process of reconstructing the shape of an object, phase information is extracted by windowing the sideband containing the phase information. The 3D information of the object is then obtained from the acquired phase information. When windowing the sideband in a narrow range, it can remove noise caused by the DC spectrum, but detailed shape information of the object is lost. On the other hand, when windowing in a wide range, detailed shape information of the object can be obtained, but noise from the DC spectrum increases. To solve this trade-off relationship, we propose a method of determining the range for windowing the sideband using a function and then averaging the windowed sidebands. This proposed method can reduce the impact of noise from the DC spectrum and enables the acquisition of the detailed shape information. In this paper, we explain the reconstruction algorithm of this method and compare it with conventional methods and the previous study, phase differences averaging (PDA). © 2024 IEEE.

关键词： Noise abatement

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Breast Tumor Classification with Fine-Tuned Hyperparameter Training using Deep Learning Models

Breast Tumor Classification with Fine-Tuned Hyperparameter T...

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2025 AI-Driven Smart Healthcare for Society 5.0, AdSoc5.0 2025

作者： Maini, Yash Singh, Sanjay Kumar Saxena, Pranshu University School of Automation and Robotics Guru Gobind Singh Indraprastha University East Delhi Campus Surajmal Vihar Delhi110092 India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9798331536336

Breast tumor classification plays a crucial role in early detection and treatment planning for breast cancer. In recent years, deep learning models have shown remarkable success in image-based medical diagnosis. This study focuses on improving breast tumor classification using fine-tuned hyperparameter training of deep learning models. By optimizing key parameters such as learning rate, batch size, and network architecture, the classification accuracy of models like ResNet, AlexNet, GoogleNet and VGG is enhanced. The proposed approach leverages advanced techniques such as transfer learning, data augmentation, and hyperparameter optimization algorithms (Bayesian Optimization along with population-based training) to achieve robust performance on breast cancer datasets. The proposed method is able to achieve 98.46% accuracy while classifying breast cancer dataset. Experimental results demonstrate significant improvements in classification accuracy, precision, recall, and F1-score compared to conventional approaches, making the fine-tuned models more suitable for clinical applications. The findings suggest that hyperparameter tuning in deep learning models is critical to improving model performance and generalization in breast tumor classification tasks. © 2025 IEEE.

关键词： Deep learning

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Transforming Classification with Federated Learning on Blockchain: A Unique Model Integration Approach

Transforming Classification with Federated Learning on Block...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Hao, Zhihao Zhang, Bob Li, Haisheng School of Computer Science and Artificial Intelligence Beijing Technology and Business University Beijing China Dept. of Computer and Information Science Faculty of Science and Technology University of Macau Taipa China Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation University of Macau Taipa China

ISBN: (纸本)9798350368741

The need for robust machine learning models is particularly evident in the realm of biological pattern recognition. Traditional centralized methods often struggle, as they frequently depend on large datasets that are challenging to gather due to stringent data privacy regulations. To address these limitations while maintaining classification accuracy, we propose an innovative approach that unifies models with diverse architectures within a federated learning framework built on a blockchain network. This decentralized and trustworthy system fosters effective collaboration among various models. Furthermore, we have implemented a weight distribution mechanism designed to maximize the individual strengths of each model. By leveraging blockchains inherent transparency and auditability, this approach also ensures secure and traceable data exchanges among participants. Additionally, the adaptability of the framework allows it to be extended to other domains where privacy-preserving data sharing is critical. Our experimental results showcase that the proposed methodology significantly enhances performance in classification tasks compared to existing alternatives. © 2025 IEEE.

关键词： blockchain collaborative training Federated Learning heterogeneous models

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Active perception for grasp detection via neural graspness field 24

Active perception for grasp detection via neural graspness f...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Haoxiang Ma Modi Shi Boyang Gao Di Huang State Key Laboratory of Complex and Critical Software Environment School of Computer Science and Engineering Beihang University Beijing China Geometry Robotics

ISBN: (纸本)9798331314385

This paper tackles the challenge of active perception for robotic grasp detection in cluttered environments. Incomplete 3D geometry information can negatively affect the performance of learning-based grasp detection methods, and scanning the scene from multiple views introduces significant time costs. To achieve reliable grasping performance with efficient camera movement, we propose an active grasp detection framework based on the Neural Graspness Field (NGF), which models the scene incrementally and facilitates next-best-view planning. Constructed in real-time as the camera moves, the NGF effectively models the grasp distribution in 3D space by rendering graspness predictions from each view. For next-best-view planning, we aim to reduce the uncertainty of the NGF through a graspness inconsistency-guided policy, selecting views based on discrepancies between NGF outputs and a pre-trained graspness network. Additionally, we present a neural graspness sampling method that decodes graspness values from the NGF to improve grasp pose detection results. Extensive experiments on the GraspNet-1Billion benchmark demonstrate significant performance improvements compared to previous works. Real-world experiments show that our method achieves a superior trade-off between grasping performance and time costs. Code is available at https://***/mahaoxiang822/ActiveNGF.

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Text-Guided Image Generation for Railway Intrusion Anomaly Detection

Text-Guided Image Generation for Railway Intrusion Anomaly D...

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2023 IEEE International Conference on Unmanned Systems, ICUS 2023

作者： Chen, Jinghang Zhang, Chi Feng, Xiaodan Liu, Yuehu Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China School of Computer Science and Technology Xi'an Jiaotong University Xi'an China

ISBN: (纸本)9798350316308

Railway intrusion are characterized by strong suddenness, high unpredictability and many disturbing factors, which are key factors affecting railway safety. Currently, the deep neural network-based intrusion object detection algorithm relies on the diversity of training data. However, the data of railway intrusion image is scarce and difficult to obtain. Existing image generation methods face the problem of insufficient realism in the generated images and difficulty in generating large amounts of data. We aim to explore a text-guided image data generation framework for specific visual tasks by using an existing large language model combined with a text-to-image diffusion model. In this paper, we construct a framework for railway intrusion image generation guided by textual prompts and build a dataset of railway intrusion images based on this framework. The dataset generated in this paper contains a wide range of intrusion images and includes a variety of environmental factors such as weather and time, which provides better diversity than existing datasets. The evaluation results show that the intrusion images generated in this paper have high quality and realism. © 2023 IEEE.

关键词： Deep neural networks

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Generalizing 6-DoF Grasp Detection via Domain Prior Knowledge

arXiv

引用

arXiv 2024年

作者： Ma, Haoxiang Shi, Modi Gao, Boyang Huang, Di State Key Laboratory of Software Development Environment Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China School of Computer Science and Technology Harbin Institute of Technology Harbin China Geometry Robotics China

We focus on the generalization ability of the 6-DoF grasp detection method in this paper. While learning-based grasp detection methods can predict grasp poses for unseen objects using the grasp distribution learned from the training set, they often exhibit a significant performance drop when encountering objects with diverse shapes and structures. To enhance the grasp detection methods’ generalization ability, we incorporate domain prior knowledge of robotic grasping, enabling better adaptation to objects with significant shape and structure differences. More specifically, we employ the physical constraint regularization during the training phase to guide the model towards predicting grasps that comply with the physical rule on grasping. For the unstable grasp poses predicted on novel objects, we design a contact-score joint optimization using the projection contact map to refine these poses in cluttered scenarios. Extensive experiments conducted on the GraspNet-1billion benchmark demonstrate a substantial performance gain on the novel object set and the real-world grasping experiments also demonstrate the effectiveness of our generalizing 6-DoF grasp detection method. Code is available at https://***/mahaoxiang822/ Generalizing-Grasp. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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A Hybrid CNN-GRU model for Session-based Recommender Systems 9

A Hybrid CNN-GRU model for Session-based Recommender Systems

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9th International Conference on computer and Communications, ICCC 2023

作者： Tegene, Abebe Tamrat Liu, Qiao Gan, Yanglei Jimale, Elias Ayenew, Melak Leka, Habte University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China Addis Ababa Science and Technology University Center of Excellence of Ai and Robotics Addis Ababa Ethiopia University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China

ISBN: (纸本)9798350317251

In many e-commerce recommendation systems, user profiles are mostly not available. In such cases, the recommendation task considers user behaviors like click sessions or browsing history. Traditional recommender methods often rely on users' past interactions for recommending user interests, which is difficult to apply to session-based recommender systems. However, these methods perform very poorly on this task. Recently, deep learning techniques such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs) showed outstanding performance in session-based recommendation problems. In this paper, we propose a hybrid CNN and gated recurrent unit (GRU) model that exploits the advantages of both CNN and GRU in modeling the rigid order requirement for interactions inside sessions and modeling the sequential long-term dependencies in session data, respectively. Our proposed hybrid model has two architectures: the first part consists of the CNN layers that take input from Item ID to learn local features and relationships in sessions, and the second part is a GRU layer that takes the output from the CNN layer to learn the long-term dependencies in that session. This hybrid technique enables the architecture for modeling prominent features in the session to provide accurate recommendations. Extensive experiments over a real-world dataset demonstrate that the proposed hybrid model performs well and outperforms those GRU and CNN-based models in the state-of-the-art recommendation task on both metrics used. © 2023 IEEE.

关键词： Recommender systems

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