Binarized P-Network: Deep Reinforcement Learning of Robot Control from Raw Images on FPGA

Kadokawa, Yuki; Tsurumine, Yoshihisa; Matsubara, Takamitsu

doi:10.1109/lra.2021.3111416

Cited by 3 publications

(1 citation statement)

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“…Images are used as inputs in a variety of systems [1,2,3] , and techniques for extracting information from them are the foundation of many applications such as simultaneous localization and mapping (SLAM) [4], autonomous driving safety [5], and human-robot interaction [6]. However, achieving high-speed image pro-…”

Section: Introductionmentioning

confidence: 99%

Fast FPGA-Based Image Feature Extraction for Data Fusion in Autonomous Vehicles.

Gaia,

Orosco,

Rossomando

et al. 2023

Int. Jour. Eng. Ins.

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Computer vision plays a critical role in many applications, particularly in the domain of autonomous vehicles. To achieve high-level image processing tasks such as image classification and object tracking, it is essential to extract low-level features from the image data. However, in order to integrate these compute-intensive tasks into a control loop, they must be completed as quickly as possible. This paper presents a novel FPGA-based system for fast and accurate image feature extraction, specifically designed to meet the constraints of data fusion in autonomous vehicles. The system computes a set of generic statistical image features, including contrast, homogeneity, and entropy, and is implemented on two Xilinx FPGA platforms - an Alveo U200 Data Center Accelerator Card and a Zynq UltraScale+ MPSoC ZCU104 Evaluation Kit. Experimental results show that the proposed system achieves high-speed image feature extraction with low latency, making it well-suited for use in autonomous vehicle systems that require real-time image processing. The presented system can also be easily extended to extract additional features for various image and data fusion applications.

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