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Faster Metallic Surface Defect Detection Using Deep Learning with Channel Shuffling

Computers, Materials & Continua 2023年第4期75卷 1847-1861页

作者： Siddiqui Muhammad Yasir hyunsik ahn Department of Robot System Engineering Tongmyong UniversityBusan48520Korea School of Artificial Intelligence Tongmyong UniversityBusan48520Korea

Deep learning has been constantly improving in recent years,and a significant number of researchers have devoted themselves to the research of defect detection *** and recognition of small and complex targets is still a problem that needs to be *** authors of this research would like to present an improved defect detection model for detecting small and complex defect targets in steel *** steel strip production,mechanical forces and environmental factors cause surface defects of the steel ***,the detection of such defects is key to the production of high-quality ***,surface defects of the steel strip cause great economic losses to the high-tech *** far,few studies have explored methods of identifying the defects,and most of the currently available algorithms are not sufficiently ***,this study presents an improved real-time metallic surface defect detection model based on You Only Look Once(YOLOv5)specially designed for small *** the smaller features of the target,the conventional part is replaced with a depthwise convolution and channel shuffle *** assigning weights to Feature Pyramid Networks(FPN)output features and fusing them,increases feature propagation and the network’s characterization *** experimental results reveal that the improved proposed model outperforms other comparable models in terms of accuracy and detection *** precision of the proposed model achieved by mAP@0.5 is 77.5%on the Northeastern University,Dataset(NEU-DET)and 70.18%on the GC10-DET datasets.

关键词： Defect detection deep learning convolution neural network object detection YOLOv5 shuffleNetv2

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3D Instance Segmentation Using Deep Learning on RGB-D Indoor Data

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Computers, Materials & Continua 2022年第9期72卷 5777-5791页

作者： Siddiqui Muhammad Yasir Amin Muhammad Sadiq hyunsik ahn Department of Robot System Engineering Tongmyong UniversityBusan48520Korea Department of Information&Technology University of Central PunjabLahorePakistan Department of Electronics Engineering Tongmyong UniversityBusan48520Korea

3D object recognition is a challenging task for intelligent and robot systems in industrial and home indoor *** is critical for such systems to recognize and segment the 3D object instances that they encounter on a frequent *** computer vision,graphics,and machine learning fields have all given it a lot of ***,3D segmentation was done with hand-crafted features and designed approaches that didn’t achieve acceptable performance and couldn’t be generalized to large-scale *** learning approaches have lately become the preferred method for 3D segmentation challenges by their great success in 2D computer ***,the task of instance segmentation is currently less *** this paper,we propose a novel approach for efficient 3D instance segmentation using red green blue and depth(RGB-D)data based on deep *** 2D region based convolutional neural networks(Mask R-CNN)deep learning model with point based rending module is adapted to integrate with depth information to recognize and segment 3D instances of *** order to generate 3D point cloud coordinates(x,y,z),segmented 2D pixels(u,v)of recognized object regions in the RGB image are merged into(u,v)points of the depth ***,we conducted an experiment and analysis to compare our proposed method from various points of view and *** experimentation shows the proposed 3D object recognition and instance segmentation are sufficiently beneficial to support object handling in robotic and intelligent systems.

关键词： Instance segmentation 3D object segmentation deep learning point cloud coordinates

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Deep Learning-Based 3D Instance and Semantic Segmentation: A Review

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Journal on Artificial Intelligence 2022年第2期4卷 99-114页

作者： Siddiqui Muhammad Yasir hyunsik ahn Department of Robot System Engineering Tongmyong UniversityBusan48520Korea School of Artificial Intelligence Tongmyong UniversityBusan48520Korea

The process of segmenting point cloud data into several homogeneous areas with points in the same region having the same attributes is known as 3D *** is challenging with point cloud data due to substantial redundancy,fluctuating sample density and lack of apparent *** research area has a wide range of robotics applications,including intelligent vehicles,autonomous mapping and navigation.A number of researchers have introduced various methodologies and *** learning has been successfully used to a spectrum of 2D vision domains as a prevailing ***,due to the specific problems of processing point clouds with deep neural networks,deep learning on point clouds is still in its initial *** study examines many strategies that have been presented to 3D instance and semantic segmentation and gives a complete assessment of current developments in deep learning-based 3D *** these approaches’benefits,draw backs,and design mechanisms are studied and *** study evaluates the impact of various segmentation algorithms on competitiveness on various publicly accessible datasets,as well as the most often used pipelines,their advantages and limits,insightful findings and intriguing future research directions.

关键词： Artificial intelligence computer vision robot vision 3D instance segmentation 3D semantic segmentation 3D data deep learning point cloud mesh voxel RGB-D segmentation

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