Binocular Vision Images Research Articles

Research on Binocular Vision Image Calibration Method Based on Canny Operator

Research on Binocular Vision Image Calibration Method Based on Canny Operator

Multi‐object road waste detection and classification based on binocular vision

AbstractA road multi‐object detection algorithm is one of the core algorithms for intelligent road cleaning robots relying on machine vision. Most existing object detection algorithms analyse all image regions and finally calculate the category and location of each object. However, it is not necessary to analyse all areas of the image when detecting objects on the road surface where the background changes little, and the number of objects is small because there will be a lot of invalid calculations. If we can perform targeted local analysis on images instead of analysing all image regions, it will improve the detection efficiency. Therefore, this paper proposes a multi‐object detection method using a binocular camera and a convolutional neural network (CNN) that effectively reduces invalid calculations during the detection and improves detection efficiency. In the developed method, the binocular vision image acquired by the binocular camera is stereo matched and equalized, while linear regression and coordinate transformation eliminate the angle of the camera pair concerning the road surface. Then, the coordinates of the regions of interest (ROI) is calculated in the left vision image and the features within the ROI is extracted from the corresponding CNN's feature map. Next, ROI pooling resizes the extracted feature maps of different sizes to the same size, which are then input to the fully connected layers to output the results. The proposed binocular network and faster R‐CNN (VGG16) are trained and tested on a dataset involving 1000 road waste images. The experimental results demonstrate that the developed binocular network improves the detection accuracy and speed by 28.56% and 78.39%, respectively, compared with faster R‐CNN (VGG16), providing a reliable basis for a machine vision‐based intelligent road cleaning robot.

Stereo Vision-Based Relative Position and Attitude Estimation of Non-Cooperative Spacecraft

In on-orbit services, the relative position and attitude estimation of non-cooperative spacecraft has become the key issues to be solved in many space missions. Because of the lack of prior knowledge about manual marks and the inability to communicate between non-cooperative space targets, the relative position and attitude estimation system poses great challenges in terms of accuracy, intelligence, and power consumptions. To address these issues, this study uses a stereo camera to extract the feature points of a non-cooperative spacecraft. Then, the 3D position of the feature points is calculated according to the camera model to estimate the relationship. The optical flow method is also used to obtain the geometric constraint information between frames. In addition, an extended Kalman filter is used to update the measurement results and obtain more accurate pose optimization results. Moreover, we present a closed-loop simulation system, in which the Unity simulation engine is employed to simulate the relative motion of the spacecraft and binocular vision images, and a JetsonTX2 supercomputer is involved to conduct the proposed autonomous relative navigation algorithm. The simulation results show that our approach can estimate the non-cooperative target’s relative pose accurately.

A Feature Fast Matching Technique Using Improved Self-Organizing Map for Stereo Vision

Landmark feature match method of robot visual navigation has been studied, An unsupervised learning methods based on binocular vision has been proposed to match Landmark feature fast. Firstly, environmental Information images are obtained by binocular vision, Then SIFT feature vectors are abstracted from binocular vision images, Lastly, Self-Organizing Map is pulled in to match multidimensional feature point fast by using competitive learning methods. Experiments showed that the proposed methods on feature matching with better computation time and effect than the traditional SIFT and SURF of feature matching methods. And it can satisfied the requirement of real-time.

Research on 3D Modeling System Based on Binocular Vision Image Acquisition

Three-dimensional modeling of the image acquisition system based on binocular computer vision scanning system scene with MCU processing the two-dimensional image data collected by the CCD sensor, and complete the image binarization processing using hardware, to improve the efficiency of the image processing; using timing delay analysis circuit the edge of the identification image characteristics, the data amount of the compression of the image processing; will be collected by the CCD sensor the two-dimensional image data transmission to a computer, thereby effectively shortening the transmission time of the data.