Enhanced handwriting recognition through hybrid UNet-based architecture with global classical features

Journal of Ambient Intelligence and Humanized Computing, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 18, 2025

Language: Английский

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Lane segmentation and center line prediction – a multitask framework for autonomous driving systems

Transportation Planning and Technology, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 22

Published: May 13, 2025

Language: Английский

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Deep Learning-Based Lane-Keeping Assist System for Self-Driving Cars Using Transfer Learning and Fine Tuning

Journal of Advances in Information Technology, Journal Year: 2024, Volume and Issue: 15(3), P. 322 - 329

Published: Jan. 1, 2024

This paper presents an advanced lane-keeping assistance system specifically designed for self-driving cars.The proposed model combines the powerful Xception network with transfer learning and fine-tuning techniques to accurately predict steering angle.By analyzing cameracaptured images, effectively learns from human driving knowledge provides precise estimations of angle necessary safe lane-keeping.The technique allows leverage extensive acquired ImageNet dataset, while is utilized tailor pre-trained specific task prediction based on input enabling optimal performance.Fine-tuning was initiated by initially freezing training only Fully Connected (FC) layer first 10 epochs.Subsequently, entire model, encompassing both backbone FC layer, unfrozen further training.To evaluate system's effectiveness, a comprehensive comparative analysis conducted against popular existing models, including Nvidia, MobilenetV2, VGG19, InceptionV3.The evaluation includes assessment operational accuracy loss function, utilizing Mean Squared Error (MSE) equation.The achieves lowest function values validation, demonstrating its superior predictive performance.Additionally, model's performance evaluated through real-world testing pre-designed trajectories maps, resulting in minimal deviation desired trajectory over time.This practical valuable insights into mode's reliability potential assist lanekeeping tasks.

Language: Английский

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Semantic scene segmentation for indoor autonomous vision systems: leveraging an enhanced and efficient U-NET architecture

Multimedia Tools and Applications, Journal Year: 2024, Volume and Issue: unknown

Published: May 9, 2024

Language: Английский

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Steering Angle Prediction for Autonomous Vehicles Using Deep Transfer Learning

Journal of Advances in Information Technology, Journal Year: 2024, Volume and Issue: 15(1), P. 138 - 146

Published: Jan. 1, 2024

Self-driving cars are anticipated to revolutionize future transportation due their reliability, safety, and continuous learning capabilities.Researchers actively engaged in developing autonomous driving systems, employing techniques like behavioral cloning reinforcement learning.This study introduces a distinctive perspective by an end-to-end approach, using camera inputs predict steering angles based on model learned from human expertise.The demonstrates rapid training achieves over 90.1% accuracy Mean Percentage of Prediction (MPP).In this context, the aims replicate driver behavior applying transfer pre-trained VGG19 with various activation functions.The proposed is trained analyze road images as input, predicting optimal adjustments.Evaluation encompasses dataset ROS2 simulation environment, comparing results several Convolutional Neural Networks (CNNs) models including Nvidia's, MobileNet-V2, ResNet50, VGG16, VGG19.The impact functions Exponential Linear Unit (ELU), Rectified (ReLU), Leaky ReLU also explored.This research contributes advancing systems addressing real-world complexities facilitating integration into everyday transportation.The novel approach utilizing comprehensive evaluation underscores its significance optimizing self-driving technology.

Language: Английский

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Enhancing Semantic Scene Segmentation for Indoor Autonomous Systems Using Advanced Attention-Supported Improved UNet

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: July 2, 2024

This paper introduces EFFB7-UNet, an advanced semantic segmentation framework tailored for Indoor Autonomous Vision Systems (IAVSs) utilizing the U-Net architecture. The employs EfficientNetB4 as its encoder, significantly enhancing feature extraction. It integrates a spatial and channel Squeeze-and-Excitation (scSE) attention block, emphasizing critical areas features to refine outcomes. Comprehensive evaluations using NYUv2 Dataset various augmented datasets were conducted. study systematically compares EFFB7-UNet's performance with multiple encoders, including ResNet50, ResNet101, MobileNet V2, VGG16, VGG19, EfficientNets B0-B6. findings reveal that EFFB7-UNet not only surpasses these configurations in terms of accuracy but also highlights effectiveness scSE block achieving superior results. Without depth information, achieves 12\% improvement mean Intersection over Union (mIOU). notable enhancement adaptability across different domains, implying substantial progress reliability Intelligent (IAVS) technologies.

Language: Английский

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Novel hawk swarm‐optimized deep learning classification with K‐nearest neighbor based decision making for autonomous vehicle movement controller

Concurrency and Computation Practice and Experience, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 31, 2024

Summary Nowadays, intelligent transportation systems pay a lot of attention to autonomous vehicles it is believed that an vehicle improves mobility, comfort, safety, and energy efficiency. Making decisions essential for the development since these algorithms must be able manage dynamic complex urban crossings. In this research optimal deep BiLSTM‐GAN classifier detect movement smart vehicles, initially preprocessing stage performed decrease noise in received data after regions are next extracted region interest (ROI) make right decision. The forwarded GAN road segmentation as well optimized BiLSTM classifier, which recognizes traffic sign, simultaneously making possible do modified Hough line‐based maneuver prediction using segmented information from roads. Finally, determines lane, predicts sign. K‐nearest neighbor (KNN)‐based controllers used decision based on predicted sign about lane. proposed HSO algorithm was developed outcome common fusion hawk swarm optimization. Based lane detecting achievements, at training percentage (TP) 90, accuracy 91.75%, Peak signal‐to‐noise ratio (PSNR) 64.84%, mean square error (MSE) 28.78, absolute (MAE) 20.20, respectively, similarly achievements TP 93.71%, sensitivity 95.15%, specificity 93.91%, MSE 28.78%, respectively.

Language: Английский

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Improved lane detection for autonomous vehicles using deep learning, semantic segmentation, edge detection and multi-sensor data fusion

South Florida Journal of Development, Journal Year: 2024, Volume and Issue: 5(10), P. e4532 - e4532

Published: Oct. 24, 2024

Automated driving has gained significant attention because it can eliminate severe risks in real time. While autonomous vehicles rely heavily on sensors for lane detection, obstacle identification, and environmental awareness, accurate recognition remains a persistent challenge due to factors such as noise from shadows, poor markings, obstructed views. Despite advances computer vision, this problem is yet be fully resolved, presenting gap the current literature. The primary objective of research address these challenges by developing an enhanced lane-detection system. To achieve this, study integrates advanced techniques, including semantic segmentation, edge deep learning, coupled with multi-sensor data fusion cameras, LIDAR, radar. By employing methodology, examines various methods benchmarks proposed model against existing systems terms accuracy, specificity processing speed. Initial findings demonstrate that combination segmentation improves detection real-time scenarios. achieved accuracy 97.8%, 99.28%, average time 0.0047 seconds per epoch. This not only addresses limitations but also offers insights into improving road safety vehicles.

Language: Английский

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Fine-tuned depth-augmented U-Net for enhanced semantic segmentation in indoor autonomous vision systems

Journal of Real-Time Image Processing, Journal Year: 2024, Volume and Issue: 22(1)

Published: Dec. 6, 2024

Language: Английский

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