Glass, though ubiquitous, is difficult to recognize in an image due to its transparency. Fine-grained low-level features indicating the presence of glass, such as refraction and reflection, are weak and subtle. This c...
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The lack of communication options for Deaf and hearing people, some may say creates a significant social disadvantage in accessing the often-bare essential services. In contrast to acoustically communicated sound patt...
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Fault detection is of great significance in ensuring the operational safety of industrial equipment. However, in practice, it is difficult to obtain fault data because industrial equipment is in normal operation most ...
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The importance of images in today’s society has made it essential for them to be of the highest quality and visually indicative of their essential traits and attributes. Significant research has been done individuall...
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Alzheimer's is one of the progressively debilitating conditions, and it currently affects millions of people worldwide with no definitive medication for treatment. Understanding the nature of this disease is very ...
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The integration of Traffic Light Detection (TLD) systems with Advanced Emergency Braking Systems (AEBS) marks a critical milestone in enhancing road safety and paving the way for advanced autonomous driving. This surv...
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ISBN:
(纸本)9789819783540
The integration of Traffic Light Detection (TLD) systems with Advanced Emergency Braking Systems (AEBS) marks a critical milestone in enhancing road safety and paving the way for advanced autonomous driving. This survey paper provides a panoramic and extensive overview of the state-of-the-art TLD solutions leveraging sensors and deep learning techniques. With an increasing emphasis on accident prevention and traffic management, the intersection of TLD and AEBS has become a focal point of research and development. This survey begins by elucidating the fundamental challenges associated with TLD, including varying environmental conditions, occlusions, and complex traffic scenarios. We explore the pivotal role of sensors such as cameras, LiDAR, and radar in providing the requisite data for TLD, and delve into the intricacies of sensor fusion techniques for enhanced perception. Deep Learning has emerged as a cornerstone technology in TLD, enabling robust object detection, classification, and real-time decision-making. We meticulously analyze a spectrum of deep learning architectures including Single-Shot Detectors (SSD), Faster R-CNN, YOLO, and custom-designed networks tailored for TLD applications. Furthermore, the survey examines critical components of the TLD pipeline, encompassing data collection, preprocessing, model training, real-time inference, and integration with AEBS. Emphasis is placed on real-time constraints, multi-modal sensor fusion, and adaptability to diverse traffic light configurations. The paper also delves into the significance of accurate traffic light state prediction, going beyond mere detection to anticipate traffic light changes and optimize vehicle control actions. Human-centric interaction and privacy concerns are addressed, encompassing driver warnings, user interfaces, and data anonymization strategies. Moreover, the survey discusses the importance of safety, validation, and collaboration within the TLD and AEBS ecosystem, emphasizing compl
The scarcity of bilingual parallel corpus imposes limitations on exploiting the state-of-the-art supervised translation *** of the research directions is employing relations among multi-modal data to enhance ***,the r...
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The scarcity of bilingual parallel corpus imposes limitations on exploiting the state-of-the-art supervised translation *** of the research directions is employing relations among multi-modal data to enhance ***,the reliance on manually annotated multi-modal datasets results in a high cost of data *** this paper,the topic semantics of images is proposed to alleviate the above ***,topic-related images can be auto-matically collected from the Internet by search ***,topic semantics is sufficient to encode the relations be-tween multi-modal data such as texts and ***,we propose a visual topic semantic enhanced translation(VTSE)model that utilizes topic-related images to construct a cross-lingual and cross-modal semantic space,allowing the VTSE model to simultaneously integrate the syntactic structure and semantic *** the above process,topic similar texts and images are wrapped into groups so that the model can extract more robust topic semantics from a set of similar images and then further optimize the feature *** results show that our model outperforms competitive base-lines by a large margin on the Multi30k and the Ambiguous COCO *** model can use external images to bring gains to translation,improving data efficiency.
This paper provides an efficient and accurate sign language recognition system in real time that understands gestures employed using MediaPipe and Random Forest in American Sign Language (ASL). The system captures and...
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Without much assistance from a person, a driverless automobile can sense its surroundings and navigate challenges like traffic. Although it took years of discussion and development, many industries have delivered the ...
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With the rapid development of intelligent systems, Multi-Agent Systems (MAS) have shown unique advantages in solving complex decision-making problems. Particularly in the field of Multi-Agent Reinforcement Learning (M...
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