Faster Metallic Surface Defect Detection Using Deep Learning with Channel Shuffling

Deep learning has been constantly improving in recent years,and a significant number of researchers have devoted themselves to the research of defect detection algorithms.Detection and recognition of small and complex targets is still a problem that needs to be solved.The authors of this research would like to present an improved defect detection model for detecting small and complex defect targets in steel surfaces.During steel strip production,mechanical forces and environmental factors cause surface defects of the steel strip.Therefore,the detection of such defects is key to the production of high-quality products.Moreover,surface defects of the steel strip cause great economic losses to the high-tech industry.So far,few studies have explored methods of identifying the defects,and most of the currently available algorithms are not sufficiently effective.Therefore,this study presents an improved real-time metallic surface defect detection model based on You Only Look Once(YOLOv5)specially designed for small networks.For the smaller features of the target,the conventional part is replaced with a depthwise convolution and channel shuffle mechanism.Then assigning weights to Feature Pyramid Networks(FPN)output features and fusing them,increases feature propagation and the network’s characterization ability.The experimental results reveal that the improved proposed model outperforms other comparable models in terms of accuracy and detection time.The 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.

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

3D object recognition is a challenging task for intelligent and robot systems in industrial and home indoor environments.It is critical for such systems to recognize and segment the 3D object instances that they encounter on a frequent basis.The computer vision,graphics,and machine learning fields have all given it a lot of attention.Traditionally,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 data.Deep learning approaches have lately become the preferred method for 3D segmentation challenges by their great success in 2D computer vision.However,the task of instance segmentation is currently less explored.In this paper,we propose a novel approach for efficient 3D instance segmentation using red green blue and depth(RGB-D)data based on deep learning.The 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 objects.In 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 image.Moreover,we conducted an experiment and analysis to compare our proposed method from various points of view and distances.The experimentation shows the proposed 3D object recognition and instance segmentation are sufficiently beneficial to support object handling in robotic and intelligent systems.

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

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 segmentation.Segmentation is challenging with point cloud data due to substantial redundancy,fluctuating sample density and lack of apparent organization.The 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 algorithms.Deep learning has been successfully used to a spectrum of 2D vision domains as a prevailing A.I.methods.However,due to the specific problems of processing point clouds with deep neural networks,deep learning on point clouds is still in its initial stages.This 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 segmentation.In these approaches’benefits,draw backs,and design mechanisms are studied and addressed.This 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.