Real-Time Implementation of Obstacle Detection Algorithms on a Datacube MaxPCI Architecture

2002 ◽  
Vol 8 (2) ◽  
pp. 157-172 ◽  
Author(s):  
Mau-Tsuen Yang ◽  
Tarak Gandhi ◽  
Rangachar Kasturi ◽  
Lee Coraor ◽  
Octavia Camps ◽  
...  
Keyword(s):  
Real Time ◽  
Obstacle Detection ◽  
Detection Algorithms

scholarly journals Real-time Track Obstacle Detection from 3D LIDAR Point Cloud

2021 ◽  
Vol 1910 (1) ◽  
pp. 012002
Author(s):  
Chao He ◽  
Jiayuan Gong ◽  
Yahui Yang ◽  
Dong Bi ◽  
Jianpin Lan ◽  
...  
Keyword(s):  
Real Time ◽  
Point Cloud ◽  
Obstacle Detection ◽  
3D Lidar

RSNet: Rail semantic segmentation network for extracting aerial railroad images

2021 ◽  
pp. 1-18
Author(s):  
R.S. Rampriya ◽  
Sabarinathan ◽  
R. Suganya
Keyword(s):  
Real Time ◽  
Visual Processing ◽  
Feature Fusion ◽  
Semantic Segmentation ◽  
Vital Role ◽  
Obstacle Detection ◽  
Aerial Images ◽  
Computationally Efficient ◽  
Fusion Algorithm ◽  
Uav Images

In the near future, combo of UAV (Unmanned Aerial Vehicle) and computer vision will play a vital role in monitoring the condition of the railroad periodically to ensure passenger safety. The most significant module involved in railroad visual processing is obstacle detection, in which caution is obstacle fallen near track gage inside or outside. This leads to the importance of detecting and segment the railroad as three key regions, such as gage inside, rails, and background. Traditional railroad segmentation methods depend on either manual feature selection or expensive dedicated devices such as Lidar, which is typically less reliable in railroad semantic segmentation. Also, cameras mounted on moving vehicles like a drone can produce high-resolution images, so segmenting precise pixel information from those aerial images has been challenging due to the railroad surroundings chaos. RSNet is a multi-level feature fusion algorithm for segmenting railroad aerial images captured by UAV and proposes an attention-based efficient convolutional encoder for feature extraction, which is robust and computationally efficient and modified residual decoder for segmentation which considers only essential features and produces less overhead with higher performance even in real-time railroad drone imagery. The network is trained and tested on a railroad scenic view segmentation dataset (RSSD), which we have built from real-time UAV images and achieves 0.973 dice coefficient and 0.94 jaccard on test data that exhibits better results compared to the existing approaches like a residual unit and residual squeeze net.

scholarly journals A Real-Time Apple Targets Detection Method for Picking Robot Based on Improved YOLOv5

2021 ◽  
Vol 13 (9) ◽  
pp. 1619
Author(s):  
Bin Yan ◽  
Pan Fan ◽  
Xiaoyan Lei ◽  
Zhijie Liu ◽  
Fuzeng Yang
Keyword(s):  
Real Time ◽  
Apple Tree ◽  
Detection Method ◽  
Recognition Algorithm ◽  
Medium Size ◽  
Feature Maps ◽  
Detection Algorithms ◽  
Fusion Mode ◽  
Visual Attention Mechanism ◽  
Tree Image

The apple target recognition algorithm is one of the core technologies of the apple picking robot. However, most of the existing apple detection algorithms cannot distinguish between the apples that are occluded by tree branches and occluded by other apples. The apples, grasping end-effector and mechanical picking arm of the robot are very likely to be damaged if the algorithm is directly applied to the picking robot. Based on this practical problem, in order to automatically recognize the graspable and ungraspable apples in an apple tree image, a light-weight apple targets detection method was proposed for picking robot using improved YOLOv5s. Firstly, BottleneckCSP module was improved designed to BottleneckCSP-2 module which was used to replace the BottleneckCSP module in backbone architecture of original YOLOv5s network. Secondly, SE module, which belonged to the visual attention mechanism network, was inserted to the proposed improved backbone network. Thirdly, the bonding fusion mode of feature maps, which were inputs to the target detection layer of medium size in the original YOLOv5s network, were improved. Finally, the initial anchor box size of the original network was improved. The experimental results indicated that the graspable apples, which were unoccluded or only occluded by tree leaves, and the ungraspable apples, which were occluded by tree branches or occluded by other fruits, could be identified effectively using the proposed improved network model in this study. Specifically, the recognition recall, precision, mAP and F1 were 91.48%, 83.83%, 86.75% and 87.49%, respectively. The average recognition time was 0.015 s per image. Contrasted with original YOLOv5s, YOLOv3, YOLOv4 and EfficientDet-D0 model, the mAP of the proposed improved YOLOv5s model increased by 5.05%, 14.95%, 4.74% and 6.75% respectively, the size of the model compressed by 9.29%, 94.6%, 94.8% and 15.3% respectively. The average recognition speeds per image of the proposed improved YOLOv5s model were 2.53, 1.13 and 3.53 times of EfficientDet-D0, YOLOv4 and YOLOv3 and model, respectively. The proposed method can provide technical support for the real-time accurate detection of multiple fruit targets for the apple picking robot.

Fault detection algorithms for real-time diagnosis in large-scale systems

2001 ◽  
Author(s):  
Thiagalingam Kirubarajan ◽  
Venkatesh N. Malepati ◽  
Somnath Deb ◽  
Jie Ying
Keyword(s):  
Fault Detection ◽  
Real Time ◽  
Large Scale ◽  
Large Scale Systems ◽  
Detection Algorithms ◽  
Time Diagnosis

Real-time obstacle detection with motion features using monocular vision

2014 ◽  
Vol 31 (3) ◽  
pp. 281-293 ◽  
Author(s):  
Baozhi Jia ◽  
Rui Liu ◽  
Ming Zhu
Keyword(s):  
Real Time ◽  
Obstacle Detection ◽  
Monocular Vision ◽  
Motion Features

Field Assessment of Camera Based Drilling Dynamics

2021 ◽  
Author(s):  
Alexis Koulidis ◽  
Mohamed Abdullatif ◽  
Ahmed Galal Abdel-Kader ◽  
Mohammed-ilies Ayachi ◽  
Shehab Ahmed ◽  
...  
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Real Time ◽  
Tracking Algorithm ◽  
Interest Points ◽  
Stick Slip ◽  
Detection Algorithms ◽  
Point Tracking ◽  
Surface Data ◽  
Field Assessment

Abstract Surface data measurement and analysis are an established mean of detecting drillstring low-frequency torsional vibration or stick-slip. The industry has also developed models that link surface torque and downhole drill bit rotational speed. Cameras provide an alternative noninvasive approach to existing wired/wireless sensors used to gather such surface data. The results of a preliminary field assessment of drilling dynamics utilizing camera-based drillstring monitoring are presented in this work. Detection and timing of events from the video are performed using computer vision techniques and object detection algorithms. A real-time interest point tracker utilizing homography estimation and sparse optical flow point tracking is deployed. We use a fully convolutional deep neural network trained to detect interest points and compute their accompanying descriptors. The detected points and descriptors are matched across video sequences and used for drillstring rotation detection and speed estimation. When the drillstring's vibration is invisible to the naked eye, the point tracking algorithm is preceded with a motion amplification function based on another deep convolutional neural network. We have clearly demonstrated the potential of camera-based noninvasive approaches to surface drillstring dynamics data acquisition and analysis. Through the application of real-time object detection algorithms on rig video feed, surface events were detected and timed. We were also able to estimate drillstring rotary speed and motion profile. Torsional drillstring modes can be identified and correlated with drilling parameters and bottomhole assembly design. A novel vibration array sensing approach based on a multi-point tracking algorithm is also proposed. A vibration threshold setting was utilized to enable an additional motion amplification function providing seamless assessment for multi-scale vibration measurement. Cameras were typically devices to acquire images/videos for offline automated assessment (recently) or online manual monitoring (mainly), this work has shown how fog/edge computing makes it possible for these cameras to be "conscious" and "intelligent," hence play a critical role in automation/digitalization of drilling rigs. We showcase their preliminary application as drilling dynamics and rig operations sensors in this work. Cameras are an ideal sensor for a drilling environment since they can be installed anywhere on a rig to perform large-scale live video analytics on drilling processes.

Real-time obstacle detection for Unmanned Surface Vehicle

2011 ◽  
Author(s):  
Han Wang ◽  
Zhuo Wei ◽  
Sisong Wang ◽  
Chek Seng Ow ◽  
Kah Tong Ho ◽  
...  
Keyword(s):  
Real Time ◽  
Obstacle Detection ◽  
Unmanned Surface Vehicle

scholarly journals Obstacle detection for autonomous systems using stereoscopic images and bacterial behaviour

2020 ◽  
Vol 10 (2) ◽  
pp. 2164
Author(s):  
Fredy Martinez ◽  
Edwar Jacinto ◽  
Fernando Martinez
Keyword(s):  
Real Time ◽  
High Performance ◽  
Time Estimation ◽  
Obstacle Detection ◽  
Stereoscopic Vision ◽  
Human Interaction ◽  
Human Model ◽  
Service Robots ◽  
Indoor Environments ◽  
Time Operation

This paper presents a low cost strategy for real-time estimation of the position of obstacles in an unknown environment for autonomous robots. The strategy was intended for use in autonomous service robots, which navigate in unknown and dynamic indoor environments. In addition to human interaction, these environments are characterized by a design created for the human being, which is why our developments seek morphological and functional similarity equivalent to the human model. We use a pair of cameras on our robot to achieve a stereoscopic vision of the environment, and we analyze this information to determine the distance to obstacles using an algorithm that mimics bacterial behavior. The algorithm was evaluated on our robotic platform demonstrating high performance in the location of obstacles and real-time operation.

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