Oct 4, 2024 · Popular algorithms used to perform object detection include convolutional neural networks (R-CNN, Region-Based Convolutional Neural Networks), Fast R-CNN, and YOLO …

This paper aims at providing a solution to improve the overall quality of real-time object detection computer vision algorithms using a hybrid hardware–software implementation. To this end, we …

Aug 21, 2021 · Object detection is a key component of many deep learning models, and has undergone a number of revolutionary transformations in recent years. Object detection refers …

This repository provides an up-to-date the list of studies, which is the advanced work of object detection based on FPGAs. We plan to update the platform in the long term. It follows the …

In this paper, we present a novel pipelined NMS algorithm that eliminates this sequential dependency and associated NMS latency overhead. We then use our novel NMS algorithm to …

Jan 17, 2025 · This paper provides a detailed analysis of the training and inference post processes, accelerating the algorithms from a software–hardware co-design perspective.

1 day ago · Through software and hardware co-design, integrating the optimized YOLOv3 algorithm with Zynq's hardware acceleration considerably enhances the operational efficiency …

Instructions in this tutorial are provided for three hardware configurations so you can choose those that fit your system's configuration, whether your system uses: A CPU. A GPU. The Intel ® …

We show that our GPU-based detector is up to 90 times faster than a well-optimized software version and can be easily trained on millions of examples. Using inexpensive off-the-shelf …

Feb 5, 2024 · By training an object detection model, it is possible to identify and detect multiple objects, making it a versatile approach. Object detection plays a crucial role in various …