Robotic grasping method of bolster spring based on image-based visual servoing with YOLOv3 object detection algorithm

2021 ◽  
pp. 095440622110197
Author(s):  
Dafa Li ◽  
Huanlong Liu ◽  
Tao Wei ◽  
Jianyi Zhou
Keyword(s):  
Object Detection ◽  
Visual Servoing ◽  
Control Method ◽  
Image Features ◽  
Geometric Features ◽  
Robotic Grasping ◽  
Bounding Box ◽  
Corner Points ◽  
Cluttered Background ◽  
Automatic Positioning

In this paper, to address the problem of automatic positioning and grasping of bolster spring with complex geometric features and cluttered background, a novel image-based visual servoing (IBVS) control method based on the corner points features of YOLOv3 object detection bounding box is proposed and applied to the robotic grasping system of bolster spring. The YOLOv3 object detection model is used to detect and position the bolster spring and then based on the corner points features of the bolster spring bounding box, the IBVS controller is designed to drive the end effector of the robot to the desired pose. This method adopts the approach-align-grasp control strategy to achieve the grasping of the bolster spring, which is very robust to the calibration parameter errors of the camera and the robot model. With the help of Robotics and Machine Vision Toolboxes in Matlab, IBVS simulation analysis based on feature points is carried out. The results show that it is reasonable to use the corner points of the object detection bounding box as image features under the initial pose where the image plane is parallel to the object plane. The positioning and grasping experiments are conducted on the robotic grasping platform of bolster spring. The results show that this method is effective for automatic positioning and grasping of bolster spring with complex geometric features and cluttered background, and it has strong robustness to the change of illumination.

Automatic accounting of Baikal diatomic algae: approaches and prospects

2019 ◽  
pp. 295-299
Author(s):  
Кonstantin А. Elshin ◽  
Еlena I. Molchanova ◽  
Мarina V. Usoltseva ◽  
Yelena V. Likhoshway
Keyword(s):  
Object Detection ◽  
Loss Function ◽  
Classification Accuracy ◽  
Diatom Species ◽  
Bounding Box ◽  
Synedra Acus ◽  
And Training

Using the TensorFlow Object Detection API, an approach to identifying and registering Baikal diatom species Synedra acus subsp. radians has been tested. As a result, a set of images was formed and training was conducted. It is shown that аfter 15000 training iterations, the total value of the loss function was obtained equal to 0,04. At the same time, the classification accuracy is equal to 95%, and the accuracy of construction of the bounding box is also equal to 95%.

scholarly journals A new multi-scale backbone network for object detection based on asymmetric convolutions

2021 ◽  
Vol 104 (2) ◽  
pp. 003685042110113
Author(s):  
Xianghua Ma ◽  
Zhenkun Yang
Keyword(s):  
Object Detection ◽  
Image Features ◽  
Detection Accuracy ◽  
Mobile Platforms ◽  
Multi Scale ◽  
Backbone Network ◽  
Aspect Ratios ◽  
Pascal Voc ◽  
Scale Characteristics ◽  
Detection Speed

Real-time object detection on mobile platforms is a crucial but challenging computer vision task. However, it is widely recognized that although the lightweight object detectors have a high detection speed, the detection accuracy is relatively low. In order to improve detecting accuracy, it is beneficial to extract complete multi-scale image features in visual cognitive tasks. Asymmetric convolutions have a useful quality, that is, they have different aspect ratios, which can be used to exact image features of objects, especially objects with multi-scale characteristics. In this paper, we exploit three different asymmetric convolutions in parallel and propose a new multi-scale asymmetric convolution unit, namely MAC block to enhance multi-scale representation ability of CNNs. In addition, MAC block can adaptively merge the features with different scales by allocating learnable weighted parameters to three different asymmetric convolution branches. The proposed MAC blocks can be inserted into the state-of-the-art backbone such as ResNet-50 to form a new multi-scale backbone network of object detectors. To evaluate the performance of MAC block, we conduct experiments on CIFAR-100, PASCAL VOC 2007, PASCAL VOC 2012 and MS COCO 2014 datasets. Experimental results show that the detection precision can be greatly improved while a fast detection speed is guaranteed as well.

Visual servoing of dynamic wheeled mobile robots with anti-interference finite-time controllers

2018 ◽  
Vol 38 (5) ◽  
pp. 558-567 ◽  
Author(s):  
Hua Chen ◽  
Lei Chen ◽  
Qian Zhang ◽  
Fei Tong
Keyword(s):  
Mobile Robots ◽  
Finite Time ◽  
Visual Servoing ◽  
Control Method ◽  
External Disturbance ◽  
Nonholonomic Systems ◽  
Stability Control ◽  
Interference Control ◽  
Wheeled Mobile Robots ◽  
Content Type

Purpose The finite-time visual servoing control problem is considered for dynamic wheeled mobile robots (WMRs) with unknown control direction and external disturbance. Design/methodology/approach By using finite-time control method and switching design technique. Findings First, the visual servoing kinematic WMR model is developed, which can be converted to the dynamic chained-form systems by using a state and input feedback transformation. Then, for two decoupled subsystems of the chained-form systems, according to the finite-time stability control theory, a discontinuous three-step switching control strategy is proposed in the presence of uncertain control coefficients and external disturbance. Originality/value A class of discontinuous anti-interference control method has been presented for the dynamic nonholonomic systems.

Rotational multipyramid network with bounding‐box transformation for object detection

2021 ◽  
Author(s):  
Donggeun Kim ◽  
San Kim ◽  
Siheon Jeong ◽  
Ji‐Wan Ham ◽  
Seho Son ◽  
...  
Keyword(s):  
Object Detection ◽  
Bounding Box

Multi-branch Bounding Box Regression for Object Detection

2022 ◽  
Author(s):  
Hui-Shen Yuan ◽  
Si-Bao Chen ◽  
Bin Luo ◽  
Hao Huang ◽  
Qiang Li
Keyword(s):  
Object Detection ◽  
Bounding Box

Concepts of Augmented Image Space and Transformed Feature Space for Efficient Visual Servoing of an “Eye-in-Hand Robot”

Robotica ◽  
1991 ◽  
Vol 9 (2) ◽  
pp. 203-212 ◽  
Author(s):  
Won Jang ◽  
Kyungjin Kim ◽  
Myungjin Chung ◽  
Zeungnam Bien
Keyword(s):  
Visual Feedback ◽  
Visual Servoing ◽  
Industrial Robot ◽  
Robot Vision ◽  
Feature Space ◽  
Image Features ◽  
Image Space ◽  
Formal Definition ◽  
Computational Burden ◽  
Definition Of

SUMMARYFor efficient visual servoing of an “eye-in-hand” robot, the concepts of Augmented Image Space and Transformed Feature Space are presented in the paper. A formal definition of image features as functionals is given along with a technique to use defined image features for visual servoing. Compared with other known methods, the proposed concepts reduce the computational burden for visual feedback, and enhance the flexibility in describing the vision-based task. Simulations and real experiments demonstrate that the proposed concepts are useful and versatile tools for the industrial robot vision tasks, and thus the visual servoing problem can be dealt with more systematically.

scholarly journals Improved Oriented Object Detection in Remote Sensing Images Based on a Three-Point Regression Method

2021 ◽  
Vol 13 (22) ◽  
pp. 4517
Author(s):  
Falin Wu ◽  
Jiaqi He ◽  
Guopeng Zhou ◽  
Haolun Li ◽  
Yushuang Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Poor Performance ◽  
Regression Method ◽  
Remote Sensing Images ◽  
Sensing Applications ◽  
Bounding Box ◽  
Bounding Boxes ◽  
Fully Connected ◽  
Oriented Object

Object detection in remote sensing images plays an important role in both military and civilian remote sensing applications. Objects in remote sensing images are different from those in natural images. They have the characteristics of scale diversity, arbitrary directivity, and dense arrangement, which causes difficulties in object detection. For objects with a large aspect ratio and that are oblique and densely arranged, using an oriented bounding box can help to avoid deleting some correct detection bounding boxes by mistake. The classic rotational region convolutional neural network (R2CNN) has advantages for text detection. However, R2CNN has poor performance in the detection of slender objects with arbitrary directivity in remote sensing images, and its fault tolerance rate is low. In order to solve this problem, this paper proposes an improved R2CNN based on a double detection head structure and a three-point regression method, namely, TPR-R2CNN. The proposed network modifies the original R2CNN network structure by applying a double fully connected (2-fc) detection head and classification fusion. One detection head is for classification and horizontal bounding box regression, the other is for classification and oriented bounding box regression. The three-point regression method (TPR) is proposed for oriented bounding box regression, which determines the positions of the oriented bounding box by regressing the coordinates of the center point and the first two vertices. The proposed network was validated on the DOTA-v1.5 and HRSC2016 datasets, and it achieved a mean average precision (mAP) of 3.90% and 15.27%, respectively, from feature pyramid network (FPN) baselines with a ResNet-50 backbone.

scholarly journals Vision-Based Target Detection and Tracking for a Miniature Pan-Tilt Inertially Stabilized Platform

Electronics ◽  
2021 ◽  
Vol 10 (18) ◽  
pp. 2243
Author(s):  
Jianchuan Guo ◽  
Chenhu Yuan ◽  
Xu Zhang ◽  
Fan Chen
Keyword(s):  
Target Detection ◽  
Feedback Linearization ◽  
Visual Servoing ◽  
Control Method ◽  
Sliding Mode ◽  
Optical Axis ◽  
Detection And Tracking ◽  
The Stability ◽  
Stabilized Platform ◽  
The Mathematical Model

This paper presents a novel visual servoing sheme for a miniature pan-tilt intertially stabilized platform (ISP). A fully customized ISP can be mounted on a miniature quadcopter to achieve stationary or moving target detection and tracking. The airborne pan-tilt ISP can effectively isolate a disturbing rotational motion of the carrier, ensuring the stabilization of the optical axis of the camera in order to obtain a clear video image. Meanwhile, the ISP guarantees that the target is always on the optical axis of the camera, so as to achieve the target detection and tracking. The vision-based tracking control design adopts a cascaded control structure based on the mathematical model, which can accurately reflect the dynamic characteristics of the ISP. The inner loop of the proposed controller employs a proportional lag compensator to improve the stability of the optical axis, and the outer loop adopts the feedback linearization-based sliding mode control method to achieve the target tracking. Numerical simulations and laboratory experiments demonstrate that the proposed controller can achieve satisfactory tracking performance.

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