Deep Learning Model Compression and Hardware Acceleration for High-Performance Foreign Material Detection on Poultry Meat Using NIR Hyperspectral Imaging

Sensors, Год журнала: 2025, Номер 25(3), С. 970 - 970

Опубликована: Фев. 6, 2025

Ensuring the safety and quality of poultry products requires efficient detection removal foreign materials during processing. Hyperspectral imaging (HSI) offers a non-invasive mechanism to capture detailed spatial spectral information, enabling discrimination different types contaminants from muscle non-muscle external tissues. When integrated with advanced deep learning (DL) models, HSI systems can achieve high accuracy in detecting materials. However, dimensionality data, computational complexity DL high-paced nature processing environments pose challenges for real-time implementation industrial settings, where speed decision-making is critical. In this study, we address these by optimizing inference HSI-based material through combination post-training quantization hardware acceleration techniques. We leveraged utilizing TensorRT module NVIDIA GPU enhance speed. Additionally, applied half-precision (called FP16) reduce precision model parameters, decreasing memory usage requirements without any loss accuracy. conducted simulations using two hypothetical hyperspectral line-scan cameras evaluate feasibility conditions. The simulation results demonstrated that our optimized models could times compatible line speeds lines between 140 250 birds per minute, indicating potential deployment. Specifically, proposed method, compression, achieved reductions time up five compared unoptimized, traditional GPU-based inference. addition, it resulted 50% decrease size while maintaining was also comparable original model. Our findings suggest integration an effective strategy overcoming bottlenecks associated on data.

Язык: Английский

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Digital twin-enabled post-disaster damage and recovery monitoring with deep learning: leveraging transfer learning, attention mechanisms, and explainable AI

Geomatics Natural Hazards and Risk, Год журнала: 2025, Номер 16(1)

Опубликована: Апрель 2, 2025

Язык: Английский

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Optimized Hybrid Deep Learning Framework for Early Detection of Alzheimer’s Disease Using Adaptive Weight Selection

Diagnostics, Год журнала: 2024, Номер 14(24), С. 2779 - 2779

Опубликована: Дек. 11, 2024

Background: Alzheimer’s disease (AD) is a progressive neurological disorder that significantly affects middle-aged and elderly adults, leading to cognitive deterioration hindering daily activities. Notwithstanding progress, conventional diagnostic techniques continue be susceptible inaccuracies inefficiencies. Timely precise diagnosis essential for early intervention. Methods: We present an enhanced hybrid deep learning framework amalgamates the EfficientNetV2B3 with Inception-ResNetV2 models. The models were integrated using adaptive weight selection process informed by Cuckoo Search optimization algorithm. procedure commences pre-processing of neuroimaging data guarantee quality uniformity. Features are subsequently retrieved from utilizing algorithm allocates weights various dynamically, contingent upon their efficacy in particular tasks. achieves balanced usage distinct characteristics both through iterative configuration. This method improves classification accuracy, especially early-stage disease. A thorough assessment was conducted on extensive datasets verify framework’s efficacy. Results: attained Scott’s Pi agreement score 0.9907, indicating exceptional accuracy dependability, identifying stages results show its superiority over current state-of-the-art techniques.Conclusions: indicate substantial potential proposed as reliable scalable instrument identification effectively mitigates shortcomings algorithms complementing capabilities optimized mechanism. facilitate application across many circumstances, hence extending utility wider array datasets. capacity accurately identify facilitating prompt therapies, which crucial decelerating development enhancing patient outcomes.

Язык: Английский

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A comprehensive survey on intrusion detection algorithms

Computers & Electrical Engineering, Год журнала: 2024, Номер 121, С. 109863 - 109863

Опубликована: Ноя. 23, 2024

Язык: Английский

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