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| 1 | Three-Dimensional Measurement Using Structured Light Based on Deep Learning显示文摘Three-dimensional(3D)reconstruction using structured light projection has the characteristics of non-contact,high precision,easy operation,and strong real-time performance.However,for actual measurement,projection modulated images are disturbed by electronic noise or other interference,which reduces the precision of the measurement system.To solve this problem,a 3D measurement algorithm of structured light based on deep learning is proposed.The end-to-end multi-convolution neural network model is designed to separately extract the coarse-and fine-layer features of a 3D image.The point-cloud model is obtained by nonlinear regression.The weighting coefficient loss function is introduced to the multi-convolution neural network,and the point-cloud data are continuously optimized to obtain the 3D reconstruction model.To verify the effectiveness of the method,image datasets of different 3D gypsum models were collected,trained,and tested using the above method.Experimental results show that the algorithm effectively eliminates external light environmental interference,avoids the influence of object shape,and achieves higher stability and precision.The proposed method is proved to be effective for regular objects. | Tao Zhang Jinxing Niu Shuo Liu Taotao Pan Brij B.Gupta | 2021 | Computer Systems Science & Engineering2021，36，1： | 1 |
| 2 | UnderwaterWaste Recognition and Localization Based on Improved YOLOv5显示文摘With the continuous development of the economy and society,plastic pollution in rivers,lakes,oceans,and other bodies of water is increasingly severe,posing a serious challenge to underwater ecosystems.Effective cleaning up of underwater litter by robots relies on accurately identifying and locating the plastic waste.However,it often causes significant challenges such as noise interference,low contrast,and blurred textures in underwater optical images.A weighted fusion-based algorithm for enhancing the quality of underwater images is proposed,which combines weighted logarithmic transformations,adaptive gamma correction,improved multi-scale Retinex(MSR)algorithm,and the contrast limited adaptive histogram equalization(CLAHE)algorithm.The proposed algorithm improves brightness,contrast,and color recovery and enhances detail features resulting in better overall image quality.A network framework is proposed in this article based on the YOLOv5 model.MobileViT is used as the backbone of the network framework,detection layer is added to improve the detection capability for small targets,self-attention and mixed-attention modules are introduced to enhance the recognition capability of important features.The cross stage partial(CSP)structure is employed in the spatial pyramid pooling(SPP)section to enrich feature information,and the complete intersection over union(CIOU)loss is replaced with the focal efficient intersection over union(EIOU)loss to accelerate convergence while improving regression accuracy.Experimental results proved that the target recognition algorithm achieved a recognition accuracy of 0.913 and ensured a recognition speed of 45.56 fps/s.Subsequently,Using red,green,blue and depth(RGB-D)camera to construct a system for identifying and locating underwater plastic waste.Experiments were conducted underwater for recognition,localization,and error analysis.The experimental results demonstrate the effectiveness of the proposed method for identifying and locating underwater plastic waste,and it has good localization accuracy. | Jinxing Niu Shaokui Gu Junmin Du Yongxing Hao | 2023 | Computers, Materials & Continua2023，76，8： | 1 |
| 3 | Real-Time Dense Reconstruction of Indoor Scene显示文摘Real-time dense reconstruction of indoor scenes is of great research value for the application and development of service robots,augmented reality,cultural relics conservation and other fields.ORB-SLAM2 method is one of the excellent open source algorithms in visual SLAM system,which is often used in indoor scene reconstruction.However,it is time-consuming and can only build sparse scene map by using ORB features to solve camera pose.In view of the shortcomings of ORB-SLAM2 method,this article proposes an improved ORB-SLAM2 solution,which uses a direct method based on light intensity to solve the camera pose.It can greatly reduce the amount of computation,the speed is significantly improved by about 5 times compared with the ORB feature method.A parallel thread of map reconstruction is added with surfel model,and depth map and RGB map are fused to build the dense map.A Realsense D415 sensor is used as RGB-D cameras to obtain the three-dimensional(3D)point clouds of an indoor environments.After calibration and alignment processing,the sensor is applied in the reconstruction experiment of indoor scene with the improved ORB-SLAM2 method.Results show that the improved ORB-SLAM2 algorithm cause a great improvement in processing speed and reconstructing density of scenes. | Jinxing Niu Qingsheng Hu Yi Niu Tao Zhang Sunil Kumar Jha | 2021 | Computers, Materials & Continua2021，，9： | 0 |
| 4 | Identification of atypical T4SS effector proteins mediating bacterial defense显示文摘To remain competitive,proteobacteria use various contact-dependent weapon systems to defend against microbial com-petitors.The bacterial-killing type IV secretion system(T4SS)is one such powerful weapon.It commonly controls the killing/competition between species by secreting the lethal T4SS effector(T4E)proteins carrying conserved XVIPCD domains into competing cells.In this study,we sought knowledge to understand whether the bacterial-killing T4SS-producing bacteria encode T4E-like proteins and further explore their biological functions.To achieve this,we designed a T4E-guided approach to discover T4E-like proteins that are designated as atypical T4Es.Initially,this approach required scientists to perform simple BlastP search to identify T4E homologs that lack the XVIPCD domain in the genomes of T4SS-producing bacteria.These homologous genes were then screened in Escherichia coli to identify antibacterial candidates(atypical T4Es)and their neighboring detoxification proteins,followed by testing their gene cotranscription and validating their physical interactions.Using this approach,we did discover two atypical T4E proteins from the plant-beneficial Lysobacter enzymogenes and the phytopathogen Xanthomonas citri.We also provided substantial evidence to show that the atypical T4E protein Le1637-mediated bacterial defense in interspecies interactions between L.enzymogenes and its competitors.Therefore,the newly designed T4E-guided approach holds promise for detecting functional atypical T4E proteins in bacterial cells. | Xi Shen Zixiang Yang Zihan Li Dan Xiong Jinxing Liao Weimei He Danyu Shen Xiaolong Shao Ben Niu Yongxing He Yong-Gui Gao Guoliang Qian | 2023 | mLife2023，2，3： | 0 |
| 5 | Research on Multi-View Image Reconstruction Technology Based on Auto-Encoding Learning显示文摘Traditional three-dimensional(3D)image reconstruction method,which highly dependent on the environment and has poor reconstruction effect,is easy to lead to mismatch and poor real-time performance.The accuracy of feature extraction from multiple images affects the reliability and real-time performance of 3D reconstruction technology.To solve the problem,a multi-view image 3D reconstruction algorithm based on self-encoding convolutional neural network is proposed in this paper.The algorithm first extracts the feature information of multiple two-dimensional(2D)images based on scale and rotation invariance parameters of Scale-invariant feature transform(SIFT)operator.Secondly,self-encoding learning neural network is introduced into the feature refinement process to take full advantage of its feature extraction ability.Then,Fish-Net is used to replace the U-Net structure inside the self-encoding network to improve gradient propagation between U-Net structures,and Generative Adversarial Networks(GAN)loss function is used to replace mean square error(MSE)to better express image features,discarding useless features to obtain effective image features.Finally,an incremental structure from motion(SFM)algorithm is performed to calculate rotation matrix and translation vector of the camera,and the feature points are triangulated to obtain a sparse spatial point cloud,and meshlab software is used to display the results.Simulation experiments show that compared with the traditional method,the image feature extraction method proposed in this paper can significantly improve the rendering effect of 3D point cloud,with an accuracy rate of 92.5%and a reconstruction complete rate of 83.6%. | Tao Zhang Shaokui Gu Jinxing Niu Yi Cao | 2022 | Computers, Materials & Continua2022，，9： | 0 |
| 6 | Real-Time Recognition and Location of Indoor Objects显示文摘Object recognition and location has always been one of the research hotspots in machine vision.It is of great value and significance to the development and application of current service robots,industrial automation,unmanned driving and other fields.In order to realize the real-time recognition and location of indoor scene objects,this article proposes an improved YOLOv3 neural network model,which combines densely connected networks and residual networks to construct a new YOLOv3 backbone network,which is applied to the detection and recognition of objects in indoor scenes.In this article,RealSense D415 RGB-D camera is used to obtain the RGB map and depth map,the actual distance value is calculated after each pixel in the scene image is mapped to the real scene.Experiment results proved that the detection and recognition accuracy and real-time performance by the new network are obviously improved compared with the previous YOLOV3 neural network model in the same scene.More objects can be detected after the improvement of network which cannot be detected with the YOLOv3 network before the improvement.The running time of objects detection and recognition is reduced to less than half of the original.This improved network has a certain reference value for practical engineering application. | Jinxing Niu Qingsheng Hu Yi Niu Tao Zhang Sunil Kumar Jha | 2021 | Computers, Materials & Continua2021，，8： | 0 |
| 7 | A 3D Measurement Method Based on Coded Image显示文摘The binocular stereo vision system is often used to reconstruct 3D point clouds of an object.However,it is challenging to find effective matching points in two object images with similar color or less texture.This will lead to mismatching by using the stereo matching algorithm to calculate the disparity map.In this context,the object can’t be reconstructed precisely.As a countermeasure,this study proposes to combine the Gray code fringe projection with the binocular camera as well as to generate denser point clouds by projecting an active light source to increase the texture of the object,which greatly reduces the reconstruction error caused by the lack of texture.Due to the limitation of the camera viewing angle,a one-perspective binocular camera can only reconstruct the 2.5D model of an object.To obtain the 3D model of an object,point clouds obtained from multiple-view images are processed by coarse registration using the coarse SAC-IA algorithm and fine registration using the ICP algorithm,which is followed by voxel filtering fusion of the point cloud.To improve the reconstruction quality,a polarizer is mounted in front of the cameras to filter out the redundant reflected light.Eventually,the 3D model and the dimension of a vase are obtained after calibration. | Jinxing Niu Yayun Fu Qingsheng Hu Shaojie Yang Tao Zhang Sunil Kumar Jha | 2021 | Computers, Materials & Continua2021，，11： | 0 |
| 8 | Image Deblurring of Video Surveillance System in Rainy Environment显示文摘Video surveillance system is used in various fields such as transportation and social life.The bad weather can lead to the degradation of the video surveillance image quality.In rainy environment,the raindrops and the background are mixed,which lead to make the image degradation,so the removal of the raindrops has great significance for image restoration.In this article,after analyzing the inter-frame difference method in detecting and removing raindrops,a background difference method is proposed based on Gaussian model.In this method,the raindrop is regarded as a moving object relative to the background.The principle and procedure of the method are given to detect and remove raindrops.The parameters of the single Gaussian background model are studied in this article.The important parameter of the learning rate of Gaussian model is explored in order to better detection and removal of raindrops.Experiment shows that the results of removal of raindrops by using the proposed algorithm are better than that by using the inter-frame difference method.The image processing effect is the best when the learning rate is 0.6.The research results can provide technical reference for similar research on eliminating the influence of rainy weather. | Jinxing Niu Yajie Jiang Yayun Fu Tao Zhang Nicola Masini | 2020 | Computers, Materials & Continua2020，，10： | 0 |
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