Object detection and Tracking (OdT) represents a critical area within computervision, with broad applications across various industries, particularly in the automation of logistics operations. However, significant ch...
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ISBN:
(数字)9798331531973
ISBN:
(纸本)9798331531980
Object detection and Tracking (OdT) represents a critical area within computervision, with broad applications across various industries, particularly in the automation of logistics operations. However, significant challenges persist, including the logistics-specific datasets and the inefficiency of data annotation, which is crucial for training robust artificial intelligence models for parcel detection and tracking. These challenges are exacerbated when human experts are required to manually assign unique identifiers to objects across frames, hindering the scalability of AI model training. In this research, we present a novel deep learning-based OdT framework, leveraging Convolutional Neural Networks (CNN) integrated with evaluation metrics such as Euclidean distance and Intersection over Union (IoU) to facilitate robust object detection and tracking. To address the limitations of manual annotation, we develop an automated framework for assigning unique identifiers to bounding boxes across sequential frames. The framework is based on three logistics-relateddatasets, comprising time-series images of packages on moving conveyor belts. The first two datasets, containing 100 and 1,021 images, are manually annotated, while the thirddataset of 5,046 images is automatically annotated using the proposed tool. We also utilize the Hungarian algorithm to optimize identity assignment across frames. The results demonstrate that the framework achieves an accuracy of 99.09% in object detection and tracking with a precision of 99.25% and a recall of 99.10% for the manually annotateddataset. The automated annotation illustrates an accuracy of 98.70%. These findings indicate the robustness and scalability of the proposed framework in automating parcel tracking for logistics operations.
These days, a lot of towns struggle with traffic jams during peak hours, which increment contamination, commotion, and feelings of anxiety for the general population. Because neural networks (NN) and machine learning ...
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This exploration paper investigates the operation of deep underpinning literacy(drL) for enabling independent drone navigation in cluttered surroundings. Navigating drones in cluttered spaces poses significant challen...
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ISBN:
(数字)9798350389449
ISBN:
(纸本)9798350389456
This exploration paper investigates the operation of deep underpinning literacy(drL) for enabling independent drone navigation in cluttered surroundings. Navigating drones in cluttered spaces poses significant challenges due to the presence of obstacles anddynamic environmental conditions. Traditional navigation approaches frequently struggle to acclimatize to these complications. In this study, we propose a new frame using drL ways to enable drones to autonomously navigate through cluttered surroundings while avoiding obstacles. The frame employs a deep neural network to learn a policy that guides the drone’s conduct grounded on environmental compliances. Through expansive simulations andreal-world trials, we demonstrate the efficacity of the proposed approach in achieving robust and adaptive drone navigation in cluttered surroundings. The findings of this exploration have significant counteraccusations for colorful operations, including hunt anddeliverance operations, surveillance, and package delivery, where independent drone navigation in cluttered spaces is pivotal.
Machine learning is used in many fields because of its rapiddevelopment. To determine their efficacy and suitability for certain activities, these models must be evaluated. This study maps the knowledge domain of mac...
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ISBN:
(数字)9798350379990
ISBN:
(纸本)9798350391558
Machine learning is used in many fields because of its rapiddevelopment. To determine their efficacy and suitability for certain activities, these models must be evaluated. This study maps the knowledge domain of machine learning approaches to evaluate performance, revealing its important components. Performance metrics-quantitative measurements of machine learning model performance are examined first. Predictive and generalization metrics include accuracy, precision, recall, F1-score, and area under the rOC curve. Performance evaluation requires understanding these indicators' calculation and interpretation. Cross-validation, holdout validation, k-fold cross-validation, and bootstrapping are examined next. These methods reveal the model's performance on unseen data andreduce overfitting and underfitting. This study also addresses hyperparameter tweaking. Optimizing machine learning model hyperparameters including learning rate, regularization parameters, and network design is crucial to performance. Find the optimal hyperparameter configuration using grid search, random search, and Bayesian optimization. Finally, model selection criteria determine the best model for a task. Performance measurements, complexity, and computational resources help make decisions. This study maps the knowledge domain of using machine learning techniques to performance evaluation, revealing the fundamental features. This knowledge helps researchers and practitioners evaluate machine learning models and make educatedreal-world judgements.
These days, a lot of towns struggle with traffic jams during peak hours, which increment contamination, commotion, and feelings of anxiety for the general population. Because neural networks (NN) and machine learning ...
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ISBN:
(数字)9798350354379
ISBN:
(纸本)9798350354386
These days, a lot of towns struggle with traffic jams during peak hours, which increment contamination, commotion, and feelings of anxiety for the general population. Because neural networks (NN) and machine learning (ML) techniques can handle large numbers of parameters in massive amounts of data anddynamic behaviour over time, they are replacing analytical and statistical methods in the solving of real-world problems. This research presents a convergence of machine-learning (ML) anddeep-learning (dL) calculations for traffic stream prediction; this paves the way for flexible traffic signals, which can be implemented through the use of a calculation that adjusts the timing in accordance with the anticipated traffic flow or by controlling traffic signals to some degree. The proposed ML anddL models are constructed, validated, and tested using two publicly accessible datasets. The first depicts the total number of cars subjected to checks using various sensors at six junctions over the course of 56 days. In this research, ML anddL models are created using data from four of the six junctions. The excellent performance metrics obtained by all ML anddL computations suggest that their application to intelligent traffic light management is feasible.
Nucleardetection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovation in this area, we setup a community-...
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Super-resolution (Sr) is a fundamental computervision task that aims to obtain a high-resolution clean image from the given low-resolution counterpart. This paperreviews the NTIrE 2021 Challenge on Video Super-resol...
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