Many institutions have recently embraced biometric security solutions, utilizing biological measurements to safeguard against fraudulent activities, theft, and various security threats. Face recognition technology hol...
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ISBN:
(纸本)9798350350708;9798350350715
Many institutions have recently embraced biometric security solutions, utilizing biological measurements to safeguard against fraudulent activities, theft, and various security threats. Face recognition technology holds a pivotal role within the realm of bio-metric security systems, serving purposes such as authentication, monitoring, individual identification, and identity verification. This article aims to delve into the examination of facial recognition systems grounded in deep learning. This focus arises due to the intricate nature of the process and the existence of numerous hurdles and variables that impact algorithm performance. The objective here is to illuminate the foremost challenges that real-world systems encounter, often overlooked in previous research. Additionally,under these challenges, the article will conduct a comparative analysis of the performance of prominent facial recognition algorithms, namely VGGFace, FaceNet, and ArcFace. This academic approach will allow to make informed choices when selecting the most suitable algorithms for specific applications.
To address the challenges of manual transmission line inspection and UAV line-following flights, this study presents a systematic UAV line tracking method based on binocular vision. The proposed method involves severa...
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In an intelligent completion system, the surface control part not only needs to provide power to the downhole equipment, but also needs to communicate with the downhole equipment. In this paper, a power line carrier c...
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This paper addresses the growing interest in deploying deep learning models directly in-sensor. We present "Q-Segment", a quantized real-time segmentation algorithm, and conduct a comprehensive evaluation on...
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ISBN:
(纸本)9798350383638;9798350383645
This paper addresses the growing interest in deploying deep learning models directly in-sensor. We present "Q-Segment", a quantized real-time segmentation algorithm, and conduct a comprehensive evaluation on a low-power edge vision platform with an in-sensors processor, the Sony IMX500. One of the main goals of the model is to achieve end-to-end image segmentation for vessel-based medical diagnosis. Deployed on the IMX500 platform, Q-Segment achieves ultra-low inference time in-sensor only 0.23 ms and power consumption of only 72mW. We compare the proposed network with state-of-the-art models, both float and quantized, demonstrating that the proposed solution outperforms existing networks on various platforms in computing efficiency, e.g., by a factor of 75x compared to ERFNet. The network employs an encoder-decoder structure with skip connections, and results in a binary accuracy of 97.25 % and an Area Under the Receiver Operating Characteristic Curve (AUC) of 96.97 % on the CHASE dataset. We also present a comparison of the IMX500 processing core with the Sony Spresense, a low-power multi-core ARM Cortex-M microcontroller, and a single-core ARM Cortex-M4 showing that it can achieve in-sensor processing with end-to-end low latency (17 ms) and power consumption (254mW). This research contributes valuable insights into edge-based image segmentation, laying the foundation for efficient algorithms tailored to low-power environments. [GRAPHICS] .
For direction-of-arrival estimation problems, deep learning (DL) has shown excellent performance recently owing to the effectiveness and robustness to complicated ***, DL is always requiring massive data and lacks exp...
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Task scheduling is key to multi-platform multi-sensor systems, which faces challenges of complex sensor combination and high computational complexity. To deal with these challenges, this research introduces the concep...
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CNN and Transformer have their own excellent performance in image super-resolution, but these methods are difficult to be applied to the field of image SR alone due to the challenges of balancing model performance and...
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Recent advancements in artificial intelligence algorithms for medical imaging show significant potential in automating the detection of lung infections from chest radiograph scans. However, current approaches often fo...
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ISBN:
(纸本)9798350349405;9798350349399
Recent advancements in artificial intelligence algorithms for medical imaging show significant potential in automating the detection of lung infections from chest radiograph scans. However, current approaches often focus solely on either 2-D or 3-D scans, failing to leverage the combined advantages of both modalities. Moreover, conventional slice-based methods place a manual burden on radiologists for slice selection. To overcome these challenges, we propose the Recurrent 3-D Multi-level Vision Transformer (R3DM-ViT) model, capable of handling multimodal data to enhance diagnostic accuracy. Our quantitative evaluations demonstrate that R3DM-ViT surpasses existing methods, achieving an impressive accuracy of 96.67%, F1-score of 96.88%, mean average precision of 96.75%, and mean average recall of 97.02%. This research signifies a significant stride forward in the automated detection of lung infections through multimodal imaging.
Hyperspectral image unmixing estimates a collection of constituent materials (called endmembers) and their corresponding proportions (called abundances), which is a critical preprocessing step in many remote sensing a...
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Face detection algorithms based on deep learning approaches have made significant advancements in the last decade. However, recognising faces in dense crowd scenes remains challenging, particularly in situations with ...
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