Safety with Convenience: Applying Low Cost Obstacle Detection Technology to Powered Closure Systems with Express Motion

Abstract Traffic accidents on highways are attributed mostly to the "invisibility" of oncoming traffic and road signs. "Speeding" also causes drivers to reduce the effective radius of the vehicle path in the curve, thus trespassing into the lane of the oncoming traffic. The main aim of this paper was to develop a multisensory obstacle-detection device that is affordable, easy to implement and easy to maintain to reduce the risk of road accidents at blind corners. An ultrasonic sensor module with a maximum measuring angle of 15° was used to ensure that a significant portion of the lane was detected at the blind corner. The sensor covered a minimum effective area of 0.5 m2 of the road for obstacle detection. Yellow light was employed to signify caution while negotiating the blind corner. Two photoresistors (PRs) were used as sensors because of the limited number of pins on the microcontroller (Arduino Uno). However, the device developed for this project achieved obstacle detection at blind corners at relatively low cost and can be accessed by all road users. In real-world applications, the use of piezoelectric accelerometers (vibration sensors) instead of PR sensors would be more desirable in order to detect not only cars but also two-wheelers.

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A Vision-Based Gap Detection Method during the Placement of Large-Scale Composite Structures

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.186.11 ◽

2011 ◽

Vol 186 ◽

pp. 11-15

Author(s):

Li Cao ◽

Wen Chen ◽

Jun Xiao

Keyword(s):

Image Processing ◽

Video Processing ◽

Composite Structures ◽

Large Scale ◽

Low Cost ◽

High Quality ◽

Detection Technology ◽

Gap Measurement ◽

Automatic Placement ◽

On Line

Video processing technology is regarded as a low-cost detection technology in complex environment. Because the placement layer is thin and the surface is complex that causes high detection error and high cost in laser measurement. Two problems must be solved before using it in large-scale composite structures automatic placement. One is to obtain the high-quality and stable image, and the other is to improve efficiency of image processing. In this paper, a method obtaining the high quality placement gap images was studied. It made use of the optical characteristics of composite material’s surface texture. And some parameters were determined by experiments. To reduce the calculation cost of image processing, a placement gap measurement method based on line scanning was also proposed here. The method was effective in our detection experiments on an actual workpiece.

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Electroanalytical Biosensors for Circulating Tumor DNA Detection—A Brief Review

10.20944/preprints202111.0357.v1 ◽

2021 ◽

Author(s):

Zhijia Peng ◽

Xiaogang Lin ◽

Weiqi Nian ◽

Xiaodong Zheng ◽

Jayne Wu

Keyword(s):

Real Time ◽

Point Of Care ◽

Low Cost ◽

High Sensitivity ◽

High Specificity ◽

Circulating Tumor Dna ◽

Minimal Invasive ◽

Diagnosis And Treatment ◽

Detection Technology ◽

Tumor Dna

Early diagnosis and treatment have always been highly desired in the fight against cancer, and detection of circulating tumor DNA (ctDNA) has recently been touted as highly promising for early cancer screening. Consequently, the detection of ctDNA in liquid biopsy gains much attention in the field of tumor diagnosis and treatment, which has also attracted research interest from the industry. However, traditional gene detection technology is difficult to achieve low cost, real-time and portable measurement of ctDNA. Electroanalytical biosensors have many unique advantages such as high sensitivity, high specificity, low cost and good portability. Therefore, this review aims to discuss the latest development of biosensors for minimal-invasive, rapid, and real-time ctDNA detection. Various ctDNA sensors are reviewed with respect to their choices of receptor probes, detection strategies and figures of merit. Aiming at the portable, real-time and non-destructive characteristics of biosensors, we analyze their development in the Internet of Things, point-of-care testing, big data and big health.

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A Hands-Free Obstacle Detection and Avoidance System

Engineering, Technology & Applied Science Research ◽

10.48084/etasr.562 ◽

2015 ◽

Vol 5 (3) ◽

pp. 801-804

Author(s):

M. Abdul-Niby ◽

M. Alameen ◽

O. Irscheid ◽

M. Baidoun ◽

H. Mourtada

Keyword(s):

Obstacle Avoidance ◽

Visually Impaired ◽

Low Cost ◽

Cost Effective ◽

Obstacle Detection ◽

Text To Speech ◽

Voice Signal ◽

Visually Impaired People ◽

Impaired People ◽

The Voice

In this paper, we present a low cost hands-free detection and avoidance system designed to provide mobility assistance for visually impaired people. An ultrasonic sensor is attached to the jacket of the user and detects the obstacles in front. The information obtained is transferred to the user through audio messages and also by a vibration. The range of the detection is user-defined. A text-to-speech module is employed for the voice signal. The proposed obstacle avoidance device is cost effective, easy to use and easily upgraded.

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A Low-Cost Monocular Vision-Based Obstacle Avoidance Using SVM and Optical Flow

Unmanned Systems ◽

10.1142/s2301385018500097 ◽

2018 ◽

Vol 06 (04) ◽

pp. 267-275

Author(s):

Ajay Shankar ◽

Mayank Vatsa ◽

P. B. Sujit

Keyword(s):

Optical Flow ◽

Autonomous Navigation ◽

Low Cost ◽

Obstacle Detection ◽

Monocular Vision ◽

Support Vector ◽

Svm Classifier ◽

Range Finding ◽

Image Patches ◽

Computational Resources

Development of low-cost robots with the capability to detect and avoid obstacles along their path is essential for autonomous navigation. These robots have limited computational resources and payload capacity. Further, existing direct range-finding methods have the trade-off of complexity against range. In this paper, we propose a vision-based system for obstacle detection which is lightweight and useful for low-cost robots. Currently, monocular vision approaches used in the literature suffer from various environmental constraints such as texture and color. To mitigate these limitations, a novel algorithm is proposed, termed as Pyramid Histogram of Oriented Optical Flow ([Formula: see text]-HOOF), which distinctly captures motion vectors from local image patches and provides a robust descriptor capable of discriminating obstacles from nonobstacles. A support vector machine (SVM) classifier that uses [Formula: see text]-HOOF for real-time obstacle classification is utilized. To avoid obstacles, a behavior-based collision avoidance mechanism is designed that updates the probability of encountering an obstacle while navigating. The proposed approach depends only on the relative motion of the robot with respect to its surroundings, and therefore is suitable for both indoor and outdoor applications and has been validated through simulated and hardware experiments.

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Low-Cost Obstacle Detection and Avoidance Robots with Swarm Intelligence

10.1007/978-981-16-4149-7_8 ◽

2021 ◽

pp. 79-93

Author(s):

Abhijit Das ◽

Divesh Pandey ◽

Aman Sharma ◽

Nitish Jha ◽

Anurag Pandey ◽

...

Keyword(s):

Swarm Intelligence ◽

Low Cost ◽

Obstacle Detection

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Design of Outdoor Fire Intelligent Alarm System Based on Image Recognition

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001420500184 ◽

2019 ◽

Vol 34 (07) ◽

pp. 2050018

Author(s):

Kun Zhou ◽

Xi Zhang

Keyword(s):

Neural Network ◽

Image Recognition ◽

Low Cost ◽

Fire Detection ◽

Human Society ◽

Alarm System ◽

Training Algorithms ◽

Fire Control ◽

Detection Technology ◽

Control Work

Fire is one of the most common serious disasters in human society. It is a kind of burning phenomenon that is out of control in time and space. When a fire occurs, how to detect the fire quickly and remove it in the budding state has become the key content of fire control work. Outdoor fire is very common in our daily life, and once it occurs without effective and timely control, it will cause huge losses. Therefore, it is particularly important to study an intelligent alarm system for outdoor fire. Generally, fire detection technology can be divided into sensor fire detection technology and image fire detection technology. Sensor fire detection technology is low cost and easy to design, but its application field is limited. Under the interference of many factors outside, misjudgement and missed judgement will occur. Image fire detection technology can achieve certain detection function through manual design of features and classifiers, but there are still defects in the application in the actual diversified environment. With the development of neural network technology in recent years, it has made great breakthroughs in the field of image recognition. Its judgment type is obtained through a large number of data training algorithms. Because of its automatic feature extraction and classification characteristics, it can effectively adapt to the external environment. Therefore, this paper proposes an end-to-end two-stream neural network model to detect fires, uses fire video on the network to train the algorithm, and then uses the fire database to test. Compared with the existing fire detection algorithms, it is found that the proposed method has good practicability and versatility, and provides a good reference for the development of fire detection technology.

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