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Airplane Object Detection in Satellite Images Based on Attention Mechanism and Multi-scale Feature Fusion 4

Airplane Object Detection in Satellite Images Based on Atten...

引用

4th International conference on robotics and computer vision, ICRCV 2022

作者： Xia, Zhiwei Liu, Jun Chen, Xiao Li, Xin Chen, Peng School of Computer Science and Engineering Wuhan Institute of Technology Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan430205 China Product Quality Supervision and Inspection Institute Hubei Electronics Information Wuhan430000 China

ISBN: (数字)9781665481700

ISBN: (纸本)9781665481700

the order of magnitude of object detection instances in satellite images is larger than that in conventional images, and many small object instances are clustered together in satellite images. Most objects in conventional object detection datasets are perpendicular to the ground, while objects in satellite images are parallel to the ground and their orientation varies greatly. Conventional detection with horizontal bounding box, a horizontal bounding box may contain multiple dense instances and may contain a lot of background information, resulting in a disproportionate proportion of the background in the detection box. the oriented bounding box helps to locate the object more accurately and obtain the azimuth information of the object more easily. therefore, in this work, we used oriented bounding boxes to detect airplane objects in satellite images. In order to target the detection of airplane objects, we produced a high-resolution dataset containing only airplane objects and more small instances. We use ResNet as backbone and FPN as Neck. By adding an SE Block module after each stage of ResNet to pay attention to important features, and connecting a pyramid convolution (PConv) module after FPN to enhance feature fusion. Cropping the input image to 1024*1024 using a sliding window allowed detection accuracy improved on DOTA v1.0(90.53 mAP) and our own dataset Satellite-Airplane2309(83.48 mAP) on the GTX TITAN X. © 2022 IEEE.

关键词： Object detection

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computer vision-Enabled Autonomous Advanced Control for Serial-Link robotic Manipulators

Computer Vision-Enabled Autonomous Advanced Control for Seri...

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Digital Futures and Transformative Technologies (ICoDT2), International conference on

作者： Ammar Mazhar Mohammad Zayan Tariq Khan Muhammad Izhan Sohail Iqbal Ahsan Ali Ahsan Tanveer Department of Mechanical and Aerospace Engineering Institute of Avionics and Aeronautics Air University Islamabad Pakistan Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing China

ISBN: (数字)9798350391862

ISBN: (纸本)9798350391879

robotics is an integral part of Industry 4.0, playing a crucial role in creating autonomous workplaces. this study explores the integration of computer vision into the control system for a serial link robotic manipulator, emphasizing intelligent control strategies. YOLOv5 algorithm is utilized for target object detection, overcoming the challenge of estimating 3D positions from a single 2D camera image without any additional sensor, despite depth ambiguity. the research demonstrates successful object position estimation with mean errors of 5% along the X-axis and 1.2% along the Y-axis. A twofingered soft robotic gripper is fabricated, showing enhanced adaptability and safety in handling a variety of objects compared to a traditional aluminum gripper. the study contributes by applying monocular vision for object detection, integrating soft robotics for enhanced handling, and aligning with key Industry 4.0 principles.

关键词： YOLO three-dimensional displays Service robots robot vision systems Soft robotics Manipulators Fourth Industrial Revolution Safety Grippers Intelligent control

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IndustrialEdgeML - End-to-end edge-based computer vision systemfor Industry 5.0 4

IndustrialEdgeML - End-to-end edge-based computer vision sys...

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4th International conference on Industry 4.0 and Smart Manufacturing, ISM 2022

作者： Wagner, Raphael Matuschek, Mario Knaack, Philipp Zwick, Michael Geiß, Manuela Software Competence Center Hagenberg GmbH Softwarepark 32a Hagenberg4232 Austria Danube Dynamics Embedded Solutions GmbH Lastenstraße 38/12.OG Linz4020 Austria

State-of-the-art object detection methods gain increasing attention in various industrial applications. However, current vision systems typically rely on cloud services for data storage and training of AI models. In this work, we present an end-to-end vision system for bin-picking applications at the edge of industrial premises via industrial computers, where bin-picking refers to the dedicated selection and displacement of objects into designated areas. Our system is designed for human-robot collaboration in a range of distinct challenging industrial environments with a focus on automatically learning new objects. To this end, we propose a new approach for automated oriented object detection consisting of three stages: (i) automatic training data labeling for the new object based on autoencoders, (ii) object detection using the YOLOR-S architecture, and (iii) inference of the object's orientation, more precisely, rotated bounding box detection based on attention maps using the TS-CAM algorithm. Our method achieves state-of-the-art performance, without the need for annotations of rotated bounding boxes, when compared to the single-stage architecture S2A-Net for rotated bounding box detection. In particular, the system maintains high-quality performance not only for learning all objects at once but also for incrementally learning one object after another. © 2022 the Authors. Published by ELSEVIER B.V.

关键词： Object detection

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Design of Intelligent robot for Drug Delivery

Design of Intelligent Robot for Drug Delivery

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computer, Big Data and Artificial Intelligence (ICCBD+AI), International conference on

作者： Haojiang Li Hao Li Chen Xue Jianjun Zhang School of Engineering Design Hunan Normal University Changsha China

ISBN: (数字)9798350373233

ISBN: (纸本)9798350373240

Aiming at the problems of low drug distribution efficiency and high labor costs in hospital, we design an intelligent medicine delivery robot (car). the robot uses MSP430F5529 produced by TI as the core controller; it dynamically identifies the ward number through the K210 camera module; it uses a photosensitive sensor to identify hospital path information; and it extracts motor encoder data. the rotational speed of the feedback motor is used as speed closed-loop information; the gyroscope angle sensor is used to assist the robot in differential steering for direction selection; ensuring the orderliness of the robot's operation. Finally, the MSP430 chip is used to process each sensor data and accurately control the robot in real time, so that the robot can intelligently identify environmental information and complete the drug delivery task quickly and orderly. Several simulated environment tests proved the solution works.

关键词： Hospitals Velocity control robot vision systems robot sensing systems Motors Drug delivery Stability analysis

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Teleoperation in the Age of Mixed Reality: VR, AR, and ROS Integration for Human-robot Direct Interaction

Teleoperation in the Age of Mixed Reality: VR, AR, and ROS I...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International conference on

作者： P. Nandhini P. Chellammal J.S. Jaslin S. Harthy Ruby Priya M. Uma R Kaviyaraj Department of Computer Science and Engineering Sri shanmugha college of Engineering and Technology Sankari India Department of Computer Science and Engineering J.J College of Engineering and Technology Trichy India. Department of computational intelligence SRM institute of science and technology Kattankulathur India

robotic systems that can be operated remotely are becoming more and more common in a variety of industries, including construction, disaster relief, space exploration, and industrial applications. Teleoperation, however, necessitates skilled operators who can handle sophisticated data processing and programming. this research introduces a unique teleoperation method that makes use of Virtual Reality (VR) and the robot Operating System (ROS) to overcome this problem. By creating a 3D representation of the robot’s workspace in Unity and connecting it to the actual robot with ROS, this technique optimizes control. Programming is not necessary because the robot’s vision serves as the foundation for the user interface. through ROS integration, a target point is established in Unity and sent to the robot as the end effector position. By simply setting up a new environment, reducing the requirement for knowledgeable operators, and improving situational awareness, this method may be used to any sort of robot. For even greater situational awareness, this technique may also be used with augmented reality (AR). AR can give the operator real-time information about the robot’s surroundings by superimposing digital information over the physical world. this can be achieved by including AR markers in the physical surroundings that the VR system can detect. In the operator’s field of view, the system can then superimpose information such as sensor data or robot status onto the real-world environment.

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Design and analysis of gantry robot for pick and place mechanism with Arduino Mega 2560 microcontroller and processed using pythons 4th

Design and analysis of gantry robot for pick and place mecha...

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4th International conference on Advanced Research in Mechanical, Materials and Manufacturing Engineering (ICAMMME)

作者： Sharath, G. S. Hiremath, Niranjan Manjunatha, G. REVA Univ Sch Mech Engn Bengaluru 560064 India

robots are extensively used in industries for their precision work and amount of work that one can obtain without any defects. In this paper we are using a gantry robot for as it does not occupy the floor space therefore reducing the distance for reachability of the parts and hence reducing unnecessary material for guide way. robots work in strictly defined path and there is no or very little change in such systems in order to overcome this we are using a vision based control system to make the system dynamic in nature the images are picked by using a USB camera processed images of the object is transmitted via serial communication to the Arduino Mega 2560 microcontroller and processed using pythons open source computer vision (Open CV) image to process the image captured by the USB camera to find the exact colour and to pick the object and sort it. (c) 2020 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 4th International conference on Advanced Research in Mechanical, Materials and Manufacturing Engineering-2020.

关键词： Gantry robot Machine vision Image processing Arduino microcontroller Open CV Python

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Sparse Shape Encoding for Topologically Improved Instance Segmentation

Sparse Shape Encoding for Topologically Improved Instance Se...

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canadian conference on computer and robot vision, CRV

作者： Keyi Liu James H. Elder Centre for AI & Society Centre for Vision Research York University Toronto Canada

Neurophysiological studies suggest that neurons in the intermediate visual area V4 of the primate cortex encode a sparse representation of object shape. While there are metabolic arguments for such sparse representations, there are also potential advantages for inference. Here we explore whether sparse shape encoding can yield benefits for object instance segmentation. Specifically, we encode 2D object shape using a Distance Transform Map (DTM) and learn a sparse basis for this representation. To make use of this encoding, we design and train an instance segmentation head to estimate the sparse coefficients representing the shape of each object, and then recover the estimated shape from the zero-crossing level set of the corresponding DTM. Our novel SparseShape encoding approach produces fewer topological errors than the state of the art, yields competitive mask AP on the COCO benchmark and exhibits superior generalization performance on the Cityscapes instance segmentation task. these results suggest that the sparse shape encoding observed in primate cortex has computational advantages that can benefit computer vision instance segmentation systems.

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A Design of Agricultural robotics for the use of Sowing and Planting

A Design of Agricultural Robotics for the use of Sowing and ...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), International conference on

作者： P. Chandrakala Rinkesh Badholia E. Arunkumar Mekvin Victor M. Maheswaran Ashutosh Kumar Choubey Department of Electrical and Electronics Engineering Prince Shri Venkateshwara Padmavathy Engineering College TN Department of Mechanical Engineering IES Institute of Technology and Management IES University Bhopal M.P. India Department of Electronics and Communication Engineering Karpagam College of Engineering Coimbatore TN School of Business and Management Christ University Bangalore Mechatronics Engineering dept Nehru Institute of Engineering and Technology T.M. Palayam Tamil Nadu India Baderia Global Institute of Engineering & Management Jabalpur Madhya pradesh India

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

Agricultural robots is always getting better to deal with problems like population growth, fast urbanization, fierce competition for high-quality goods, worries about protecting the environment, and a lack of skilled workers. this in-depth study looks at the main uses of farming robotic systems, covering jobs like preparing the land, sowing, planting, treating plants, gathering, estimating yields, and phenotyping. Each robot is judged on how it moves, what it will be used for, whether it has sensors, a robotic arm, or a computer vision program, as well as its development stage and where it came from. the study finds trends, possible problems, and things that stop business growth by looking at these shared traits. It also shows which countries are putting money into studying and developing (R&D) for these products. the study points out four important areas—movement systems as a whole sensor, computer vision computer programs, and communication technologies—that need more research to make smart agriculture better. the results make it clear that spending money on farming robotic systems can pay off in the long run by helping with things like accurate yield estimates and short-term benefits like keeping an eye on the harvest.

关键词： computer vision Agricultural robots Visualization Crops robot sensing systems Market research Sensor systems

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Detangling hair using feedback-driven robotic brushing 4

Detangling hair using feedback-driven robotic brushing

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4th International conference on Soft robotics (RoboSoft)

作者： Hughes, Josie Plumb-Reyes, thomas Charles, Nicholas Mahadevan, L. Rus, Daniela MIT Distributed Robot Lab Cambridge MA 02139 USA Harvard Soft Math Lab Cambridge MA USA

ISBN: (纸本)9781728177137

the brushing of hair requires a complex understanding of the interaction between soft hair fibers and the soft brushing device. It is also reliant on having both visual and tactile information. Guided by a recently developed model of soft tangled fiber bundles, we develop a method for optimizing hair brushing by robots which seeks to minimize pain and avoid the build up of jammed entanglements. Using an experimental setup with a custom force measuring sensor and a soft brush end effector, we perform closed-loop experiments on hair brushing of different curliness. this utilizes computer vision to assess the curliness of the hair, after which the hair is brushed using a closed loop controller. To demonstrate this approach hair brushing experiments have been performed on a wide variety of wigs with amount of curl. In addition to hair brushing the insight provided by this model driven approach could be applied to brushing of fibers for textiles, or animal fibers.

关键词： Hair Optical fiber sensors Visualization Force measurement Pain Force Soft robotics

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Singular Value Decomposition (SVD) Co-training: A Semi-supervised Method for Object Detection 4

Singular Value Decomposition (SVD) Co-training: A Semi-super...

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2022 4th International conference on Artificial Intelligence and computer Science, AICS 2022

作者： Fan, Siyang Wang, Rui Xu, Jingwen Wen, James Zhiqing School of Instrumentation and Optoelectronic Engineering Beihang University Beijing China Engineering Research Center for Intelligent Robotics Ji Hua Laboratory Guangdong Foshan China

Existing object detection algorithms based on Convolutional Neural Network (CNN) generally rely on supervised learning with a large amount of labeled samples, which is very time-consuming and laborious. To address the problem, we propose a semi-supervised learning method by using Singular Value Decomposition (SVD) co-training. Specifically, this paper uses two mutually independent features which are obtained by SVD to guide the co-training of two detectors in one CNN model, meanwhile an adaptive self-labeling strategy is used to ensure the quality of pseudo labels. To verify the efficacy of SVD co-training, we conducted experiments on GMU, AVD and self-made BHID datasets. the result shows that SVD co-training could achieve the accuracy comparable to full-supervised learning by using only 5% of labeled data. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： computer vision

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