For example, a Convolutional Neural Network (CNN) trained on thousands of radar echoes can recognize the unique spatial signature of a small metallic fragment, even when its signal is partially masked ...
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Detection of concealed explosives using terahertz spectral imaging and deep learning
Detecting concealed explosives and chemical threats constitutes a critical challenge in global security, yet current ...
Researchers from the Faculty of Engineering at The University of Hong Kong (HKU) have developed two innovative deep-learning ...
The optimized detection model is integrated into both a mobile application and a dedicated edge device, demonstrating that ...
Abstract: Detection of road damages is critical for road safety and efficient road maintenance. In this article, we present deep learning solution for image pothole detection with 3 popular object ...
Deep learning uses multi-layered neural networks that learn from data through predictions, error correction and parameter adjustments. It started with the ...
1 Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra, Saudi Arabia 2 InnoV'COM Laboratory-Sup'Com, University of Carthage, Ariana, Tunisia ...
Abstract: Printed circuit boards (PCBs) are key components in the electronics industry, and ensuring their integrity is essential for reliable manufacturing. Automated inspection systems based on ...
The challenge of efficiently detecting ripe and unripe strawberries in complex environments like greenhouses, marked by dense clusters of strawberries, frequent occlusions, overlaps, and fluctuating ...
ABSTRACT: Traffic monitoring plays a vital role in smart city infrastructure, road safety, and urban planning. Traditional detection systems, including earlier deep learning models, often struggle ...
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