Weld seam detection and tracking using intelligenttechniques is a common task in robotic welding. In this study, vision-based robotic welding is implemented using a 2D Camera to reduce the experimental cost in a plan...
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Weld seam detection and tracking using intelligenttechniques is a common task in robotic welding. In this study, vision-based robotic welding is implemented using a 2D Camera to reduce the experimental cost in a planar environment. Firstly, different multi-class object recognition algorithms namely Faster Region-Based Convolutional Neural Network (Faster R-CNN), Single Shot Detector (SSD) and You Look Only Once (YOLOv3) are trained with 1530 manually labeled images. To recognize the weld seam under different brightness levels addressing arc light and splash, Contrast-Limited Adaptive Histogram Equalization (CLAHE) method has been used. Further, an extensive experimental study is conducted to find the optimal hyperparameters among three deep learning models. The performance metrics like Accuracy, Root Mean Square Error, Precision, Recall, Mean Average Precision and F1-score are calculated for different test cases. Finally, homogeneous coordinate transformation is implemented to obtain robot coordinates from weld seam pixel coordinates. The analysis is also extended to detect the bad welding conditions for improper shapes and track the weld seam for good welding shapes using TAL BRABO manipulator. It was found that the weld shapes were accurately detected and tracked precisely with 99.9% accuracy using YOLOv3 than Faster R-CNN and SSD.
Greenhouse vertical rack hydroponic systems offer a sustainable and efficient solution for meeting the increasing global food demand. This paper introduces an IoT-integrated automated system designed to perform labor ...
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Image semantic segmentation is an important part of fundamental in image interpretation and computervision. With the development of convolutional neural network technology, deep learning-based image semantic segmenta...
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Image semantic segmentation is an important part of fundamental in image interpretation and computervision. With the development of convolutional neural network technology, deep learning-based image semantic segmentation methods have received more and more attention and research. At present, many excellent semantic segmentation methods have been proposed and applied in the field of remote sensing. In this paper, we summarized the semantic segmentation methods used for remote sensing image, including the traditional remote sensing image semantic segmentation methods and the methods based on deep learning, we emphasize on summarizing the remote sensing image semantic segmentation algorithms based on deep learning and classify them into different categories, and then we introduce the datasets that commonly used and data preparation methods including pre-processing and augmentation techniques. Finally, the challenges and future directions of research in this domain are analyzed and prospected. It is hoped that this study can widen the frontiers of knowledge and provide useful literature for researchers interested in advancing this field of research.
With the development of computervision technology, more and more robots are using advanced computervisiontechniques for scene environment exploration and understanding. For intelligentrobots, recognizing and under...
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The field of traditional mobile robot navigation has undergone a gradual transformation, evolving into a standardized and procedural research domain. Through a fresh cognitive perspective on this navigation process, a...
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Within the area of environmental perception, automatic navigation, object detection, and computervision are crucial and demanding fields with many applications in modern industries, such as multi-target long-term vis...
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Within the area of environmental perception, automatic navigation, object detection, and computervision are crucial and demanding fields with many applications in modern industries, such as multi-target long-term visual tracking in automated production, defect detection, and driverless robotic vehicles. The performance of computervision has greatly improved recently thanks to developments in deep learning algorithms and hardware computing capabilities, which have spawned the creation of a large number of related applications. At the same time, with the rapid increase in autonomous systems in the market, energy consumption has become an increasingly critical issue in computervision and SLAM (Simultaneous Localization and Mapping) algorithms. This paper presents the results of a detailed review of over 100 papers published over the course of two decades (1999-2024), with a primary focus on the technical advancement in computervision. To elucidate the foundational principles, an examination of typical visual algorithms based on traditional correlation filtering was initially conducted. Subsequently, a comprehensive overview of the state-of-the-art advancements in deep learning-based computervisiontechniques was compiled. Furthermore, a comparative analysis of conventional and novel algorithms was undertaken to discuss the future trends and directions of computervision. Lastly, the feasibility of employing visual SLAM algorithms in the context of autonomous vehicles was explored. Additionally, in the context of intelligentrobots for low-carbon, unmanned factories, we discussed model optimization techniques such as pruning and quantization, highlighting their importance in enhancing energy efficiency. We conducted a comprehensive comparison of the performance and energy consumption of various computervisionalgorithms, with a detailed exploration of how to balance these factors and a discussion of potential future development trends.
vision plays a peculiar role in *** information,forming a large part of the sensory information,is fed into the human brain to formulate various types of cognition and behaviours that make humans become intelligent **...
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vision plays a peculiar role in *** information,forming a large part of the sensory information,is fed into the human brain to formulate various types of cognition and behaviours that make humans become intelligent *** advances have led to the development of brain-inspired algorithms and models for machine *** of the key components of these methods is the utilization of the computational principles underlying biological ***,advanced experimental neuroscience techniques have generated different types of neural signals that carry essential visual ***,there is a high demand for mapping out functional models for reading out visual information from neural ***,we briefly review recent progress on this issue with a focus on how machine learning techniques can help in the development of models for contending various types of neural signals,from fine-scale neural spikes and single-cell calcium imaging to coarse-scale electroencephalography(EEG)and functional magnetic resonance imaging recordings of brain signals.
Developing a resilient infrastructure is crucial for nation-building by supporting innovations and promoting sustainable growth. The Kingdom of Saudi Arabia is striving to achieve the Sustainable Development Goals (SD...
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Developing a resilient infrastructure is crucial for nation-building by supporting innovations and promoting sustainable growth. The Kingdom of Saudi Arabia is striving to achieve the Sustainable Development Goals (SDGs) set by the United Nations. Industry, Innovation, and Infrastructure (I3) are some of the strategic objectives of the Kingdom's vision 2030 par with the United Nations' SDGs. The objective is focused to develop trade and transport networks for international, regional, and local connectivity with an investment of billions of dollars to establish a robust transport network and improve the existing one for enhancing road safety to reduce the costs of deaths and serious injuries. For this, a control center for automatic monitoring could be established for 24x7 monitoring of traffic violators;the key project has been named the National Center for Transportation Safety, apart from launching the "Rental Contracts" facility with the Naql portal. Moreover, the growing urban population is causing more vehicles on the roads leading to more traffic congestion which has become severe during peak hours in the major cities causing several other issues such as environmental pollution, high greenhouse gases (GHGs) including CO2 emissions, health risks to the citizen and residents, poor air quality, higher risks of road safety, more energy consumption, discomfort to the commuters, and wastage of time and other resources. Therefore, in this research, we propose an intelligent transport system (ITS) for predicting traffic congestion levels and assist commuters in taking alternative routes to avoid congestion. An intelligent model for predicting urban traffic congestion levels using xGBoost, Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM) algorithms is developed. The comparative performance analysis of the techniques concerning the performance metrics: Mean Squared Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percenta
Deep convolutional neural networks have made groundbreaking progress in various fields of computervision. However, the high storage and processing expenses associated with building these networks limit their usefulne...
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Improved picture quality is critical to the effectiveness of object recog-nition and *** consistency of those photos is impacted by night-video systems because the contrast between high-profile items and different atmo...
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Improved picture quality is critical to the effectiveness of object recog-nition and *** consistency of those photos is impacted by night-video systems because the contrast between high-profile items and different atmospheric conditions,such as mist,fog,dust *** pictures then shift in intensity,colour,polarity and consistency.A general challenge for computervision analyses lies in the horrid appearance of night images in arbitrary illumination and ambient *** recent years,target recognition techniques focused on deep learning and machine learning have become standard algorithms for object detection with the exponential growth of computer performance ***,the iden-tification of objects in the night world also poses further problems because of the distorted backdrop and dim *** Correlation aware LSTM based YOLO(You Look Only Once)classifier method for exact object recognition and deter-mining its properties under night vision was a major inspiration for this *** order to create virtual target sets similar to daily environments,we employ night images as inputs;and to obtain high enhanced image using histogram based enhancement and iterative wienerfilter for removing the noise in the *** process of the feature extraction and feature selection was done for electing the potential features using the Adaptive internal linear embedding(AILE)and uplift linear discriminant analysis(ULDA).The region of interest mask can be segmen-ted using the Recurrent-Phase Level set ***,we use deep con-volution feature fusion and region of interest pooling to integrate the presently extremely sophisticated quicker Long short term memory based(LSTM)with YOLO method for object tracking system.A range of experimentalfindings demonstrate that our technique achieves high average accuracy with a precision of 99.7%for object detection of SSAN datasets that is considerably more than that of the other standard object detection mec
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