A review of YOLO object detection based on deep learning

Y SHAO, D ZHANG, H CHU, X ZHANG, Y RAO - 电子与信息学报, 2022 - jeit.ac.cn
Object detection is one of the basic tasks and research hotspots in the field of computer
vision. The YOLO (You Only Look Once) frames object detection is a regression problem to …

[PDF][PDF] 基于深度学习的YOLO 目标检测综述

邵延华, 张铎, 楚红雨, 张晓强, 饶云波 - 电子与信息学报, 2022 - jeit.ac.cn
目标检测是计算机视觉领域的一个基础任务和研究热点. YOLO 将目标检测概括为一个回归问题
, 实现端到端的训练和检测, 由于其良好的速度-精度平衡, 近几年一直处于目标检测领域的领先 …

FPGA-based accelerator for object detection: a comprehensive survey

K Zeng, Q Ma, JW Wu, Z Chen, T Shen… - The Journal of …, 2022 - Springer
Object detection is one of the most challenging tasks in computer vision. With the advances
in semiconductor devices and chip technology, hardware accelerators have been widely …

Accelerating neural network inference on FPGA-based platforms—A survey

R Wu, X Guo, J Du, J Li - Electronics, 2021 - mdpi.com
The breakthrough of deep learning has started a technological revolution in various areas
such as object identification, image/video recognition and semantic segmentation. Neural …

Evaluating fast algorithms for convolutional neural networks on FPGAs

Y Liang, L Lu, Q Xiao, S Yan - IEEE Transactions on Computer …, 2019 - ieeexplore.ieee.org
In recent years, convolutional neural networks (CNNs) have become widely adopted for
computer vision tasks. Field-programmable gate arrays (FPGAs) have been adequately …

Teachers do more than teach: Compressing image-to-image models

Q Jin, J Ren, OJ Woodford, J Wang… - Proceedings of the …, 2021 - openaccess.thecvf.com
Abstract Generative Adversarial Networks (GANs) have achieved huge success in
generating high-fidelity images, however, they suffer from low efficiency due to tremendous …

Sparse-YOLO: Hardware/software co-design of an FPGA accelerator for YOLOv2

Z Wang, K Xu, S Wu, L Liu, L Liu, D Wang - IEEE Access, 2020 - ieeexplore.ieee.org
Convolutional neural network (CNN) based object detection algorithms are becoming
dominant in many application fields due to their superior accuracy advantage over …

Layer-specific optimization for mixed data flow with mixed precision in FPGA design for CNN-based object detectors

DT Nguyen, H Kim, HJ Lee - … on Circuits and Systems for Video …, 2020 - ieeexplore.ieee.org
Convolutional neural networks (CNNs) require both intensive computation and frequent
memory access, which lead to a low processing speed and large power dissipation …

Sextans: A streaming accelerator for general-purpose sparse-matrix dense-matrix multiplication

L Song, Y Chi, A Sohrabizadeh, Y Choi, J Lau… - Proceedings of the …, 2022 - dl.acm.org
Sparse-Matrix Dense-Matrix multiplication (SpMM) is the key operator for a wide range of
applications including scientific computing, graph processing, and deep learning …

Nn-baton: Dnn workload orchestration and chiplet granularity exploration for multichip accelerators

Z Tan, H Cai, R Dong, K Ma - 2021 ACM/IEEE 48th Annual …, 2021 - ieeexplore.ieee.org
The revolution of machine learning poses an unprecedented demand for computation
resources, urging more transistors on a single monolithic chip, which is not sustainable in …