Public bicycle sharing systems have become an increasingly popular means of transportation in many cities around the world. However, the information shown in mobile apps or websites is commonly limited to the system&#...
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Maintaining road infrastructure is essential to effective transportation systems and public safety. This research provides a new method for pothole depth estimation and automatic road crack detection using computer vi...
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ISBN:
(数字)9798350370249
ISBN:
(纸本)9798350370270
Maintaining road infrastructure is essential to effective transportation systems and public safety. This research provides a new method for pothole depth estimation and automatic road crack detection using computer vision techniques. Our method utilizes convolutional neural networks (CNNs) for classifying road images into “With Crack” or “Without Crack” categories with high accuracy. Additionally, we employ imageprocessingalgorithms to detect and highlight cracks, providing insights into their lengths and percentages. Furthermore, we introduce a monocular depth estimation model to assess pothole depths, aiding in prioritizing road repair efforts. Experimental results demonstrate the effectiveness of our approach in accurately identifying road defects and estimating their severity. This research contributes to the advancement of intelligent infrastructure management systems, enabling proactive maintenance and ensuring safer roads for communities.
The predominant function of most facial analysis systems revolves around facial alignment and eye tracking, crucial for locating key facial landmarks in images or videos. While developers have access to various models...
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ISBN:
(数字)9798350352689
ISBN:
(纸本)9798350352696
The predominant function of most facial analysis systems revolves around facial alignment and eye tracking, crucial for locating key facial landmarks in images or videos. While developers have access to various models and techniques within popular computer vision libraries, the real-time demands and challenges posed by extreme head positions, occlusions, and inadequate lighting often limit their *** this article, we propose a range of software optimization techniques and training strategies centered on data augmentation. These strategies aim to enhance a diverse array of models integral to real-time face alignment algorithms. We advocate for a broader set of evaluation criteria, enabling innovative assessments that mitigate common issues encountered in real-time monitoring *** experimental findings showcase that models developed using our suggested methodologies exhibit smoother tracking, enhanced speed, reduced size, heightened accuracy, and greater resilience in challenging conditions.
The article explores the detection of unauthorized landfills based on remote sensing data. We have developed a mathematical approach that translates ordinary images to the 2-D manifold or 3-D manifolds in stereo image...
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ISBN:
(数字)9781510645691
ISBN:
(纸本)9781510645691;9781510645684
The article explores the detection of unauthorized landfills based on remote sensing data. We have developed a mathematical approach that translates ordinary images to the 2-D manifold or 3-D manifolds in stereo images. At the same time, the posed task when developing algorithms is the transformation (convolution) of these manifolds into a one-dimensional sample. The developed method corresponds to the following two conditions: 1) preservation of the topological proximity of the elements of the original and expanded spaces, 2) preservation of correlations between the elements of the original and transformed spaces. The basic idea that underlies the mathematical support of the developed automated system is the concept of fractal sets. The concept of continuous orthogonal transformations, including the Fibonacci transform, is used as a mathematical basis for determining anomalous signal structures in the surrounding background. The problem of monitoring and decoding space images from the point of view of the synthesis of orthogonal systems with predetermined properties (speed of calculations, order of transformation, etc.) is presented. Examples of processing by the proposed algorithm are presented on the satellite images of the Moscow Region territories of the Russian Federation.
In modern times, Security and surveillance in households have become somewhat of an especially important necessity. Advancement in technology has made it possible for everyone to access or break into different houses ...
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ISBN:
(纸本)9781665456487
In modern times, Security and surveillance in households have become somewhat of an especially important necessity. Advancement in technology has made it possible for everyone to access or break into different houses easily. The main purpose of our project is to build an advanced surveillance system that can be used to detect the different faces or any movement that may occur while in the view of the surveillance camera. This system is also supported by an application that has unique features to make it more user friendly for the users. Not only is the user notified when an unauthorized entity is detected, the user is also allowed to add different faces or objects that will be ignored during the process of theft detection. This has been achieved using unsupervised machine learning where a given set of data is compared with the actual live feed from the surveillance camera to check for any anomalies in its surroundings. The Dataset or the data used in the proposed system are a few images in the format of. JPEG and. JPG which can be stored in the given location manually by the user or through the application itself. The proposed model recognizes the images in any of the available formats. The modules used in this system are powered by a strong python module named Open Cv. This module supports various face recognition algorithms such as Haar Cascade, Eigen Faces, Fischer Faces, Local Binary Pattern Histogram (LBPH), etc and this module is responsible for all the image recognition, classification, and identification. The images extracted from the dataset are real time image frames obtained from the user webcam, both are compared using the Face Recognition module in python which uses the Regions for-Convolutional Neural Network Algorithm (R-CNN) and Unsupervised learning approach to detect and differentiate between objects in Real Time. This system also includes a message transmitting feature which works with the help of the Simple Message Transfer Protocol (SMTP) module
This research investigates the application of machine learning techniques in the detection of moving vehicle registration plates. The study explores the effectiveness of various algorithms in accurately identifying an...
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ISBN:
(数字)9798350384277
ISBN:
(纸本)9798350384284
This research investigates the application of machine learning techniques in the detection of moving vehicle registration plates. The study explores the effectiveness of various algorithms in accurately identifying and localizing license plates in dynamic and real-time scenarios. By utilizing imageprocessing and pattern recognition methods, the research aims to develop a robust and efficient system capable of detecting license plates under challenging conditions, such as varying lighting conditions and different vehicle orientations. The proposed approach offers potential benefits for law enforcement, traffic monitoring, and parking management systems. Through empirical evaluations, the study aims to validate the effectiveness and reliability of the machine learning-based approach for moving vehicle registration plate detection, providing insights for future research and development in this field.
Public transport is an important feature of urban infrastructure that needs effective management so as to improve journeys for passengers and ensure maximum vehicle occupancy. The purpose of this project is to design ...
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ISBN:
(数字)9798331585600
ISBN:
(纸本)9798331585617
Public transport is an important feature of urban infrastructure that needs effective management so as to improve journeys for passengers and ensure maximum vehicle occupancy. The purpose of this project is to design an intelligent passenger counting and tracking system, which can deliver bus occupancy visualization and location information in real time. Through the combination of computer vision, machine learning, and cloud-based storage, the system guarantees the accuracy and continuous automated passenger tracking. Raspberry Pi Model 4B is the main processing unit, which collaborates with a video camera module to perform in real time object detection. Passenger boarding and alighting are precisely counted using advanced imageprocessing methods and deep learning algorithms. In this system, GPS module continuously records the position of the bus site and GSM module connects the site with reliable wireless internet (WISP) by which the actual time data can be transmitted. Collected data such as bus information, passenger counts, and geographic coordinates are archived in a cloud- based database and can be used through a dedicated mobile application. An onboard display is used by drivers to track the seat occupancy and at the same time the passengers can see the real-time availability of seats and the location of the bus through an app. Future advance will encompass data update frequency optimization and the implementation of a scalable database solution for cost saving and increased adaptability.
This paper presents a spike sorting processor based on an accurate spike clustering algorithm. The proposed spike sorting algorithm employs an L2-normalized convolutional autoencoder to extract features from the input...
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This paper presents a spike sorting processor based on an accurate spike clustering algorithm. The proposed spike sorting algorithm employs an L2-normalized convolutional autoencoder to extract features from the input, where the autoencoder is trained using the proposed spike sorting-aware loss. In addition, we propose a similarity-based K-means clustering algorithm that conditionally updates the means by observing the cosine similarity. The modified K-means algorithm exhibits better convergence and enables online clustering with higher classification accuracy. We implement a spike sorting processor based on the proposed algorithm using an efficient time-multiplexed hardware architecture in a 40-nm CMOS process. Experimental results show that the processor consumes 224.75 mu W/mm(2) when processing 16 input channels at 7.68 MHz and 0.55 v. Our design achieves 95.54% clustering accuracy, outperforming prior spike sorting processor designs.
A very active expansion of data and convenience in modern period accept to motivate extremely significant automation tasks with the advanced algorithmic models with the technologies such as Artificial Intelligence, De...
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作者:
Remigio, Adrian S.Analytics
Computing and Complex Systems Lab Asian Institute of Management Makati City Philippines
Effective computer-aided detection and diagnosis algorithms are being sought to carry out complex pattern recognition with great precision while increasing efficiency and robustness through automation. In this study, ...
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