Towards to an Embedded Edge AI Implementation for Longitudinal Rip Detection in Conveyor Belt

Klippel, Emerson; Oliveira, Ricardo Augusto de; Maslov, D. V.; Bianchi, Andrea Gomes Campos; Silva, Saul Emanuel; Garrocho, Charles Tim Batista

doi:10.5753/sbesc_estendido.2020.13096

Cited by 4 publications

(2 citation statements)

References 4 publications

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“…Mazzia et al (2020) [15] implemented an embedded edge AI solution for real-time apple detection in orchards, with the YOLOv3-tiny algorithm on three embedded platforms. Klippel et al (2020) [16] implemented an edge AI solution to detect failures in iron ore conveyor belts. Thus, work with edge AI applications increasingly contribute to the deployment of intelligent solutions on edge.…”

Section: A Edge Aimentioning

confidence: 99%

Deep-Learning-Based Visual Odometry Models for Mobile Robotics

Sousa

Meira

Oliveira

et al. 2021

Anais Estendidos Do XI Simpósio Brasileiro De Engenharia De Sistemas Computacionais (SBESC Estendido 2021)

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Odometry is a common problem in navigation systems where there is a need to estimate the position of the vehicle or carrier in the environment. To perform autonomous tasks, robotic or intelligent devices need to be aware of their position in the environment. There are many strategies to solve an odometry problem. This work explores a visual odometry solution with a deep neural network to infer the robotic vehicle's position in a known and mapped environment. The first robot, equipped with a LIDAR, IMU, and camera, maps the environment through a SLAM technique to perform this task. The data gathered by this first robot is used as ground truth to train the neural network, and later, other robots with only one camera can locate themselves in the environment. We also propose a validation and evaluation of the neural network.

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Section: A Edge Aimentioning

confidence: 99%

Deep-Learning-Based Visual Odometry Models for Mobile Robotics

Sousa

Meira

Oliveira

et al. 2021

Anais Estendidos Do XI Simpósio Brasileiro De Engenharia De Sistemas Computacionais (SBESC Estendido 2021)

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“…This article presents a comprehensive study of FPGA-based acceleration of YOLOv2-tiny [2][3], including simulation and verification of each design module using the Vivado tool provided by Xilinx and functional verification on a hardware acceleration platform built using the PYNQ-Z2 development board. The authors pay close attention to the utilization of hardware resources in FPGAs and successfully achieve significant acceleration effects.…”

Section: Introductionmentioning

confidence: 99%

Design of YOLOv2-tiny accelerator based on PYNQ-Z2 platform

yixuan,

Hu,

Liu

et al. 2023

5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023)

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Convolutional neural networks (CNNs) have been widely used in the field of image recognition. To meet the massive computational requirements of CNNs, GPUs or other intelligent computing hardware are typically used for data processing. FPGA supports parallel computing and is characterized by programmability, high performance, low energy consumption, and strong stability. In this paper, we improved and optimized the YOLOv2-Tiny algorithm by combining it with the hardware implementation based on FPGA's hardware structure. We divided the neural network tasks and preprocessed data using the 16-bit fixed-point method to reduce hardware resource consumption. By using the PYNQ-z2 development platform to accelerate the YOLOv2-Tiny CNN, we achieved target object detection and recognition. Compared with CPU (i7-10710U), the processing capacity was 2.94 times that of CPU, and the power consumption was 3.1% of CPU.

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