Severity Prediction System for Real Time Pothole Detection

Pothole Detection System using Image Processing or using Accelerometer is not a new normal. But there is no real time application which utilizes both techniques to provide us with efficient solution. We present a system which can be useful for the drivers to determine the intensity of Pothole using both Image Processing Technology and Accelerometer device-based Algorithm. The challenge in building this system was to efficiently detect a Pothole present in roads, to analyze the severity of Pothole and to provide users with information like Road Quality and best possible route. We have used various algorithms for frequency-based pothole detection. We compared the results. Apart from that, we selected the best approach suitable for achieving the project goals. We have used a Simple Differentiation-based Edge Detection Algorithm for Image Processing. The system has been built on Map Interfaces for Android devices using Android Studio, which consists of usage of Image Processing Algorithm based Python frameworks which is a sub field of Machine Learning. It is backed by powerful DBMS. This project facilitates use of most efficient technology tools to provide a good user experience, real time application, reliability and improved efficiency.

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Real-time application of critical dimension measurement of TFT-LCD pattern using a newly proposed 2D image-processing algorithm

Optics and Lasers in Engineering ◽

10.1016/j.optlaseng.2008.01.009 ◽

2008 ◽

Vol 46 (7) ◽

pp. 558-569 ◽

Cited By ~ 18

Author(s):

Jeong-Ho Lee ◽

You-Sik Kim ◽

Sung-Ryoung Kim ◽

Il-Hwan Lee ◽

Heui-Jae Pahk

Keyword(s):

Image Processing ◽

Real Time ◽

Critical Dimension ◽

Processing Algorithm ◽

Image Processing Algorithm ◽

Dimension Measurement ◽

Real Time Application

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A Real Time Image Processing Bird Repellent System Using Raspberry Pi

FUOYE Journal of Engineering and Technology ◽

10.46792/fuoyejet.v5i2.496 ◽

2020 ◽

Vol 5 (2) ◽

Author(s):

Oluwole Arowolo ◽

Adefemi A Adekunle ◽

Joshua A Ade-Omowaye

Keyword(s):

Image Processing ◽

Real Time ◽

Moving Objects ◽

Detection System ◽

Local Binary Patterns ◽

Raspberry Pi ◽

Feature Identification ◽

Data Set ◽

Haar Features ◽

Haar Feature

Rice is one of the most consumed foods in Nigeria, therefore it’s production should be on the high as to meet the demand for it. Unfortunately, the quantity of rice produced is being affected by pests such as birds on fields and sometimes in storage. Due to the activities of birds, an effective repellent system is required on rice fields. The proposed effective repellent system is made up of hardware components which are the raspberry pi for image processing, the servo motors for rotation of camera for better field of view controlled by Arduino connected to the raspberry pi, a speaker for generating predator sounds to scare birds away and software component consisting of python and Open Cv library for bird feature identification. The model was trained separately using haar features, HOG (Histogram of Oriented Gradients) and LBP (Local Binary Patterns).Haar features resulted in the highest accuracy of 76% while HOG and LBP were, 27% and 72% respectively. Haar trained model was tested with two recorded real time videos with birds, the false positives were fairly low, about 41%. This haar feature trained model can distinguish between birds and other moving objects unlike a motion detection system which detects all moving objects. This proposed system can be improved to have a higher accuracy with a larger data set of positive and negative images. Keywords—Electronic pest repeller Haar cascade classifier, ultrasonic

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Image Processing Algorithm for Real-Time Crack Inspection in Hole Expansion Test

International Journal of Precision Engineering and Manufacturing ◽

10.1007/s12541-019-00101-4 ◽

2019 ◽

Vol 20 (7) ◽

pp. 1139-1148 ◽

Cited By ~ 5

Author(s):

Seungho Choi ◽

Kwangyoon Kim ◽

Jaeho Lee ◽

Sung Hyuk Park ◽

Hye-Jin Lee ◽

...

Keyword(s):

Image Processing ◽

Real Time ◽

Processing Algorithm ◽

Image Processing Algorithm ◽

Hole Expansion ◽

Expansion Test ◽

Hole Expansion Test

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Robust Real-Time Detection of Laparoscopic Instruments in Robot Surgery Using Convolutional Neural Networks with Motion Vector Prediction

Applied Sciences ◽

10.3390/app9142865 ◽

2019 ◽

Vol 9 (14) ◽

pp. 2865 ◽

Cited By ~ 4

Author(s):

Kyungmin Jo ◽

Yuna Choi ◽

Jaesoon Choi ◽

Jong Woo Chung

Keyword(s):

Neural Networks ◽

Real Time ◽

Convolutional Neural Networks ◽

Detection System ◽

Motion Vector ◽

Detection Algorithm ◽

Surgical Instruments ◽

The Mean ◽

Real Time Detection ◽

Motion Vector Prediction

More than half of post-operative complications can be prevented, and operation performances can be improved based on the feedback gathered from operations or notifications of the risks during operations in real time. However, existing surgical analysis methods are limited, because they involve time-consuming processes and subjective opinions. Therefore, the detection of surgical instruments is necessary for (a) conducting objective analyses, or (b) providing risk notifications associated with a surgical procedure in real time. We propose a new real-time detection algorithm for detection of surgical instruments using convolutional neural networks (CNNs). This algorithm is based on an object detection system YOLO9000 and ensures continuity of detection of the surgical tools in successive imaging frames based on motion vector prediction. This method exhibits a constant performance irrespective of a surgical instrument class, while the mean average precision (mAP) of all the tools is 84.7, with a speed of 38 frames per second (FPS).

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