scholarly journals Accurate Objects Detection Using Stereo Vision Sensor for Machine Vision Applications

2018 ◽  
Vol 7 (4.11) ◽  
pp. 9
Author(s):  
RA. Hamzah ◽  
MGY. Wei ◽  
NS. Nik Anwar ◽  
AF. Kadmin ◽  
SF. Abd Gani ◽  
...  
Keyword(s):  
Machine Vision ◽  
Object Detection ◽  
Bilateral Filter ◽  
Disparity Map ◽  
Camera System ◽  
Optimization Stage ◽  
Support Size ◽  
Winner Takes All ◽  
Aggregation Step ◽  
Objects Detection

This paper presents a new algorithm for object detection using a stereo camera system, which is applicable for machine vision applications. The propose algorithm has four stages which the first stage is matching cost computation. This step acquires the preliminary result using a pixel based differences method. Then, the second stage known as aggregation step uses a guided filter with fixed window support size. This filter is efficiently reduce the noise and increase the edge properties. After that, the optimization stage uses winner-takes-all (WTA) approach which selects the smallest matching differences value and normalized it to the disparity level. The last stage in the framework uses a bilateral filter, which is effectively further reduce the remaining noise on the disparity map. This map is two-dimensional mapping of the final result which contains information of object detection and locations. Based on the standard benchmarking stereo dataset, the proposed work produces good results and performs much better compared with some recently published methods.  

scholarly journals A new function of stereo matching algorithm based on hybrid convolutional neural network

2022 ◽  
Vol 25 (1) ◽  
pp. 223
Author(s):  
Mohd Saad Hamid ◽  
Nurulfajar Abd Manap ◽  
Rostam Affendi Hamzah ◽  
Ahmad Fauzan Kadmin ◽  
Shamsul Fakhar Abd Gani ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Stereo Matching ◽  
Three Dimensional ◽  
Bilateral Filter ◽  
Disparity Map ◽  
Matching Algorithm ◽  
Matching Cost ◽  
The Difference ◽  
Aggregation Step

This paper proposes a new hybrid method between the learning-based and handcrafted methods for a stereo matching algorithm. The main purpose of the stereo matching algorithm is to produce a disparity map. This map is essential for many applications, including three-dimensional (3D) reconstruction. The raw disparity map computed by a convolutional neural network (CNN) is still prone to errors in the low texture region. The algorithm is set to improve the matching cost computation stage with hybrid CNN-based combined with truncated directional intensity computation. The difference in truncated directional intensity value is employed to decrease radiometric errors. The proposed method’s raw matching cost went through the cost aggregation step using the bilateral filter (BF) to improve accuracy. The winner-take-all (WTA) optimization uses the aggregated cost volume to produce an initial disparity map. Finally, a series of refinement processes enhance the initial disparity map for a more accurate final disparity map. This paper verified the performance of the algorithm using the Middlebury online stereo benchmarking system. The proposed algorithm achieves the objective of generating a more accurate and smooth disparity map with different depths at low texture regions through better matching cost quality.

scholarly journals Data Augmentation Methods Applying Grayscale Images for Convolutional Neural Networks in Machine Vision

2021 ◽  
Vol 11 (15) ◽  
pp. 6721
Author(s):  
Jinyeong Wang ◽  
Sanghwan Lee
Keyword(s):  
Neural Networks ◽  
Machine Vision ◽  
Object Detection ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
Image Data ◽  
Manufacturing Productivity ◽  
Smart Factories ◽  
Grayscale Images

In increasing manufacturing productivity with automated surface inspection in smart factories, the demand for machine vision is rising. Recently, convolutional neural networks (CNNs) have demonstrated outstanding performance and solved many problems in the field of computer vision. With that, many machine vision systems adopt CNNs to surface defect inspection. In this study, we developed an effective data augmentation method for grayscale images in CNN-based machine vision with mono cameras. Our method can apply to grayscale industrial images, and we demonstrated outstanding performance in the image classification and the object detection tasks. The main contributions of this study are as follows: (1) We propose a data augmentation method that can be performed when training CNNs with industrial images taken with mono cameras. (2) We demonstrate that image classification or object detection performance is better when training with the industrial image data augmented by the proposed method. Through the proposed method, many machine-vision-related problems using mono cameras can be effectively solved by using CNNs.

scholarly journals Moving Objects Detection & Recognition using Hybrid Canny Edge Detection Algorithm in Digital Image Processing

2019 ◽  
Vol 8 (9S3) ◽  
pp. 56-60
Keyword(s):  
Object Detection ◽  
Moving Objects ◽  
Detection Method ◽  
Learning Algorithm ◽  
Detection Algorithm ◽  
Moving Objects Detection ◽  
Canny Edge ◽  
Objects Detection ◽  
Canny Edge Detection Algorithm ◽  
Day By Day

Recognition and detection of an object in the watched scenes is a characteristic organic capacity. Animals and human being play out this easily in day by day life to move without crashes, to discover sustenance, dodge dangers, etc. Be that as it may, comparable PC techniques and calculations for scene examination are not all that direct, in spite of their exceptional advancement. Object detection is the process in which finding or recognizing cases of articles (for instance faces, mutts or structures) in computerized pictures or recordings. This is the fundamental task in computer. For detecting the instance of an object and to pictures having a place with an article classification object detection method usually used learning algorithm and extracted features. This paper proposed a method for moving object detection and vehicle detection.

scholarly journals A Hard Example Mining Approach for Concealed Multi-Object Detection of Active Terahertz Image

2021 ◽  
Vol 11 (23) ◽  
pp. 11241
Author(s):  
Ling Li ◽  
Fei Xue ◽  
Dong Liang ◽  
Xiaofei Chen
Keyword(s):  
Computer Vision ◽  
Object Detection ◽  
State Of The Art ◽  
Terahertz Imaging ◽  
Public Security ◽  
Counter Terrorism ◽  
Detection Algorithms ◽  
Public Dataset ◽  
The One ◽  
Objects Detection

Concealed objects detection in terahertz imaging is an urgent need for public security and counter-terrorism. So far, there is no public terahertz imaging dataset for the evaluation of objects detection algorithms. This paper provides a public dataset for evaluating multi-object detection algorithms in active terahertz imaging. Due to high sample similarity and poor imaging quality, object detection on this dataset is much more difficult than on those commonly used public object detection datasets in the computer vision field. Since the traditional hard example mining approach is designed based on the two-stage detector and cannot be directly applied to the one-stage detector, this paper designs an image-based Hard Example Mining (HEM) scheme based on RetinaNet. Several state-of-the-art detectors, including YOLOv3, YOLOv4, FRCN-OHEM, and RetinaNet, are evaluated on this dataset. Experimental results show that the RetinaNet achieves the best mAP and HEM further enhances the performance of the model. The parameters affecting the detection metrics of individual images are summarized and analyzed in the experiments.

scholarly journals Moving Object Detection Using an Object Motion Reflection Model of Motion Vectors

Symmetry ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 34 ◽  
Author(s):  
Jisang Yoo ◽  
Gyu-cheol Lee
Keyword(s):  
Object Detection ◽  
Moving Objects ◽  
Probability Model ◽  
Motion Vector ◽  
Moving Object Detection ◽  
Object Motion ◽  
Moving Object ◽  
Disparity Map ◽  
Motion Vectors ◽  
Reflection Model

Moving object detection task can be solved by the background subtraction algorithm if the camera is fixed. However, because the background moves, detecting moving objects in a moving car is a difficult problem. There were attempts to detect moving objects using LiDAR or stereo cameras, but when the car moved, the detection rate decreased. We propose a moving object detection algorithm using an object motion reflection model of motion vectors. The proposed method first obtains the disparity map by searching the corresponding region between stereo images. Then, we estimate road by applying v-disparity method to the disparity map. The optical flow is used to acquire the motion vectors of symmetric pixels between adjacent frames where the road has been removed. We designed a probability model of how much the local motion is reflected in the motion vector to determine if the object is moving. We have experimented with the proposed method on two datasets, and confirmed that the proposed method detects moving objects with higher accuracy than other methods.

scholarly journals mproved Stereo Matching Algorithm based on Census Transform and Dynamic Histogram Cost Computation

2021 ◽  
Vol 11 (8) ◽  
pp. 48-57
Author(s):  
A. F. Kadmin ◽  
◽  
R. A. Hamzah ◽  
M. N. Abd Manap ◽  
M. S. Hamid ◽  
...  
Keyword(s):  
Stereo Matching ◽  
Disparity Map ◽  
Local Method ◽  
Matching Algorithm ◽  
Census Transform ◽  
Traditional Taxonomy ◽  
Matching Cost ◽  
Winner Takes All ◽  
The Cost ◽  
Vision Algorithm

Stereo matching is a significant subject in the stereo vision algorithm. Traditional taxonomy composition consists of several issues in the stereo correspondences process such as radiometric distortion, discontinuity, and low accuracy at the low texture regions. This new taxonomy improves the local method of stereo matching algorithm based on the dynamic cost computation for disparity map measurement. This method utilised modified dynamic cost computation in the matching cost stage. A modified Census Transform with dynamic histogram is used to provide the cost volume. An adaptive bilateral filtering is applied to retain the image depth and edge information in the cost aggregation stage. A Winner Takes All (WTA) optimisation is applied in the disparity selection and a left-right check with an adaptive bilateral median filtering are employed for final refinement. Based on the dataset of standard Middlebury, the taxonomy has better accuracy and outperformed several other state-ofthe-art algorithms. Keywords—Stereo matching, disparity map, dynamic cost, census transform, local method

scholarly journals Real-time object detection in camera system using GPU

2010 ◽  
Vol 9 (1) ◽  
pp. 45-54
Author(s):  
김인수 ◽  
Choi Hyung-Il ◽  
박영재
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Camera System
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