A computer vision framework combines YOLO object detection, geometric localization, and modeled visible light communication ...
Researchers integrated an InceptionV3 convolutional neural network into the YOLOv8 object detection architecture to achieve ...
Object detection in computer vision encompasses the automatic identification and localisation of objects within images or video streams. Early approaches relied on handcrafted features and shallow ...
Vulnerable road user (VRU) detection in complex traffic scenes is challenged by the degradation of weak local details during deep feature propagation, background ...
IDDEA illustrates the shift from generic category recognition to variant-level identification, enabling defense AI systems to ...
The object detection required for machine vision applications such as autonomous driving, smart manufacturing, and surveillance applications depends on AI modeling. The goal now is to improve the ...
Selecting the right edge device for real-time AI-powered vision is a critical decision that can impact the performance, usability, and versatility of your applications. This comparison between the ...
Given computer vision’s place as the cornerstone of an increasing number of applications from ADAS to medical diagnosis and robotics, it is critical that its weak points be mitigated, such as the ...
Umashanker Bandari's co-authored IEEE Xplore paper explores a context-aware deep learning framework for accurate, efficient, real-time multi-object detection in traffic surveillance, addressing ...
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