Deep Learning for Perception and Recognition
The rapid advancement of deep learning technology has brought about transformative breakthroughs in perception and recognition systems across a wide range of applications. In addition to driving innovation in industrial sectors, it has opened up significant opportunities in fields such as intelligen...
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| Materialtyp: | Online |
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| Språk: | engelska |
| Utgiven: |
MDPI - Multidisciplinary Digital Publishing Institute
2026
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| Länkar: | ONIX_20260416T142754_9783725862665_26 |
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| _version_ | 1869520923447525376 |
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| collection | Directory of Open Access Books |
| description | The rapid advancement of deep learning technology has brought about transformative breakthroughs in perception and recognition systems across a wide range of applications. In addition to driving innovation in industrial sectors, it has opened up significant opportunities in fields such as intelligent transportation, smart cities, healthcare, and robotics. Deep learning significantly enhances the accuracy and robustness of perception and recognition systems through hierarchical feature extraction in multilayer neural networks, achieving remarkable results in areas such as soft sensing, image classification, natural language processing, and object detection. By training on large volumes of labeled data, deep learning algorithms are able to automatically learn complex feature representations and efficiently recognize objects during the perception process. As application scenarios grow more complex and data become increasingly diverse, deep learning models continue to face significant challenges in solving real-world perception and recognition problems. These challenges include ensuring model generalization when dealing with noisy, imbalanced, or limited data; enhancing performance through self-supervised, few-shot, or transfer learning in cases of insufficient labeled data; integrating information across different scales, dimensions, and modalities. |
| format | Online |
| id | doab-20.500.12854ir-175171 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1751712026-04-16T19:20:04Z Deep Learning for Perception and Recognition Wu, Gaochang Fan, Zizhu Pan, Dong Photovoltaic panel defects Defect segmentation Deformable attention Feature aggregation Posture recognition Localization FPGA Service robot Sensor fusion Steer-by-wire (SbW) systems Steering-angle prediction Autonomous vehicles (AVs) Convolutional neural network (CNN) Barrier Lyapunov function Coal–gangue detection Object distribution density measurement (ODDM) Relative resolution object scale measurement (RROSM) Label rewriting problem Compact neural network Anti-spoofing detection Swin Transformer ResNet Auto-encoder Hypergraph neural networks Hierarchical representations Node classification Emotion recognition Brain-computer interface Student learning effectiveness State estimation CT image Milling force prediction Coal quality analysis Near-infrared spectroscopy (NIRS) X-ray fluorescence (XRF) Particle size effect Image segment Data-efficient image transformer (DeiT) Landmark localization Biplanar X-ray imaging Projective geometry Hierarchical graph fusion Mid-late multilevel gender-aware strategy Multi-task learning Adversarial attack PSF Rolling shutter CMOS Blast furnace monitoring Time-series anomaly detection Adversarial autoencoder Variational mode decomposition Unsupervised learning Defect detection DINOv2 Stochastic Configuration Network Wind blades YOLOv5 network Underwater image enhancement Underwater non-uniform blur Multi-scale perception Hybrid interaction attention Non-contact sensing Sensor comparison Laser Doppler vibrometer (LDV) Electro-acoustic transducer Musical acoustics String vibration Electric guitar Waveform analysis Skeleton-based action recognition Multimodal learning Contrastive learning Frequency learning Complex-scene image enhancement Multi-type degradation dataset Feature Filter Transformer Gaussian-filter self-attention thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries The rapid advancement of deep learning technology has brought about transformative breakthroughs in perception and recognition systems across a wide range of applications. In addition to driving innovation in industrial sectors, it has opened up significant opportunities in fields such as intelligent transportation, smart cities, healthcare, and robotics. Deep learning significantly enhances the accuracy and robustness of perception and recognition systems through hierarchical feature extraction in multilayer neural networks, achieving remarkable results in areas such as soft sensing, image classification, natural language processing, and object detection. By training on large volumes of labeled data, deep learning algorithms are able to automatically learn complex feature representations and efficiently recognize objects during the perception process. As application scenarios grow more complex and data become increasingly diverse, deep learning models continue to face significant challenges in solving real-world perception and recognition problems. These challenges include ensuring model generalization when dealing with noisy, imbalanced, or limited data; enhancing performance through self-supervised, few-shot, or transfer learning in cases of insufficient labeled data; integrating information across different scales, dimensions, and modalities. 2026-04-16T19:19:58Z 2026-04-16T19:19:58Z 2026 book ONIX_20260416T142754_9783725862665_26 9783725862665 9783725862672 https://directory.doabooks.org/handle/20.500.12854/175171 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books/ https://mdpi.com/books/pdfview/book/12083 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-6267-2 10.3390/books978-3-7258-6267-2 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725862665 9783725862672 390 CH open access |
| spellingShingle | Photovoltaic panel defects Defect segmentation Deformable attention Feature aggregation Posture recognition Localization FPGA Service robot Sensor fusion Steer-by-wire (SbW) systems Steering-angle prediction Autonomous vehicles (AVs) Convolutional neural network (CNN) Barrier Lyapunov function Coal–gangue detection Object distribution density measurement (ODDM) Relative resolution object scale measurement (RROSM) Label rewriting problem Compact neural network Anti-spoofing detection Swin Transformer ResNet Auto-encoder Hypergraph neural networks Hierarchical representations Node classification Emotion recognition Brain-computer interface Student learning effectiveness State estimation CT image Milling force prediction Coal quality analysis Near-infrared spectroscopy (NIRS) X-ray fluorescence (XRF) Particle size effect Image segment Data-efficient image transformer (DeiT) Landmark localization Biplanar X-ray imaging Projective geometry Hierarchical graph fusion Mid-late multilevel gender-aware strategy Multi-task learning Adversarial attack PSF Rolling shutter CMOS Blast furnace monitoring Time-series anomaly detection Adversarial autoencoder Variational mode decomposition Unsupervised learning Defect detection DINOv2 Stochastic Configuration Network Wind blades YOLOv5 network Underwater image enhancement Underwater non-uniform blur Multi-scale perception Hybrid interaction attention Non-contact sensing Sensor comparison Laser Doppler vibrometer (LDV) Electro-acoustic transducer Musical acoustics String vibration Electric guitar Waveform analysis Skeleton-based action recognition Multimodal learning Contrastive learning Frequency learning Complex-scene image enhancement Multi-type degradation dataset Feature Filter Transformer Gaussian-filter self-attention thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries Deep Learning for Perception and Recognition |
| title | Deep Learning for Perception and Recognition |
| title_full | Deep Learning for Perception and Recognition |
| title_fullStr | Deep Learning for Perception and Recognition |
| title_full_unstemmed | Deep Learning for Perception and Recognition |
| title_short | Deep Learning for Perception and Recognition |
| title_sort | deep learning for perception and recognition |
| topic | Photovoltaic panel defects Defect segmentation Deformable attention Feature aggregation Posture recognition Localization FPGA Service robot Sensor fusion Steer-by-wire (SbW) systems Steering-angle prediction Autonomous vehicles (AVs) Convolutional neural network (CNN) Barrier Lyapunov function Coal–gangue detection Object distribution density measurement (ODDM) Relative resolution object scale measurement (RROSM) Label rewriting problem Compact neural network Anti-spoofing detection Swin Transformer ResNet Auto-encoder Hypergraph neural networks Hierarchical representations Node classification Emotion recognition Brain-computer interface Student learning effectiveness State estimation CT image Milling force prediction Coal quality analysis Near-infrared spectroscopy (NIRS) X-ray fluorescence (XRF) Particle size effect Image segment Data-efficient image transformer (DeiT) Landmark localization Biplanar X-ray imaging Projective geometry Hierarchical graph fusion Mid-late multilevel gender-aware strategy Multi-task learning Adversarial attack PSF Rolling shutter CMOS Blast furnace monitoring Time-series anomaly detection Adversarial autoencoder Variational mode decomposition Unsupervised learning Defect detection DINOv2 Stochastic Configuration Network Wind blades YOLOv5 network Underwater image enhancement Underwater non-uniform blur Multi-scale perception Hybrid interaction attention Non-contact sensing Sensor comparison Laser Doppler vibrometer (LDV) Electro-acoustic transducer Musical acoustics String vibration Electric guitar Waveform analysis Skeleton-based action recognition Multimodal learning Contrastive learning Frequency learning Complex-scene image enhancement Multi-type degradation dataset Feature Filter Transformer Gaussian-filter self-attention thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries |
| topic_facet | Photovoltaic panel defects Defect segmentation Deformable attention Feature aggregation Posture recognition Localization FPGA Service robot Sensor fusion Steer-by-wire (SbW) systems Steering-angle prediction Autonomous vehicles (AVs) Convolutional neural network (CNN) Barrier Lyapunov function Coal–gangue detection Object distribution density measurement (ODDM) Relative resolution object scale measurement (RROSM) Label rewriting problem Compact neural network Anti-spoofing detection Swin Transformer ResNet Auto-encoder Hypergraph neural networks Hierarchical representations Node classification Emotion recognition Brain-computer interface Student learning effectiveness State estimation CT image Milling force prediction Coal quality analysis Near-infrared spectroscopy (NIRS) X-ray fluorescence (XRF) Particle size effect Image segment Data-efficient image transformer (DeiT) Landmark localization Biplanar X-ray imaging Projective geometry Hierarchical graph fusion Mid-late multilevel gender-aware strategy Multi-task learning Adversarial attack PSF Rolling shutter CMOS Blast furnace monitoring Time-series anomaly detection Adversarial autoencoder Variational mode decomposition Unsupervised learning Defect detection DINOv2 Stochastic Configuration Network Wind blades YOLOv5 network Underwater image enhancement Underwater non-uniform blur Multi-scale perception Hybrid interaction attention Non-contact sensing Sensor comparison Laser Doppler vibrometer (LDV) Electro-acoustic transducer Musical acoustics String vibration Electric guitar Waveform analysis Skeleton-based action recognition Multimodal learning Contrastive learning Frequency learning Complex-scene image enhancement Multi-type degradation dataset Feature Filter Transformer Gaussian-filter self-attention thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries |
| url | ONIX_20260416T142754_9783725862665_26 |