Hardware acceleration of YOLOv7-tiny using high-level synthesis tools

Hosseiny, Adib; Jahanirad, Hadi

doi:10.1007/s11554-023-01324-5

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Simulation Results a Hardware Simulation1

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2023

2025

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Cited by 10 publications

(2 citation statements)

References 27 publications

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“…Given the absence of existing literature on FPGA implementation of YOLOv8, we referred to related models such as YOLOv2 [33] and YOLOv7-tiny [27]. We also present the results for the GTX1080Ti (GP102) using the official PyTorch API function val() from [19].…”

Section: Simulation Results a Hardware Simulationmentioning

confidence: 99%

Energy-Efficient Implementation of YOLOv8, Instance Segmentation, and Pose Detection on RISC-V SoC

Wang,

Li,

Isshiki

2024

IEEE Access

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Section: Simulation Results a Hardware Simulationmentioning

confidence: 99%

Energy-Efficient Implementation of YOLOv8, Instance Segmentation, and Pose Detection on RISC-V SoC

Wang,

Li,

Isshiki

2024

IEEE Access

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“…Furthermore, real-time performance is also highly demanded for traffic object detection algorithms. For example, Adib Hosseiny et al [16] achieved accelerated deployment of the YOLOv7-tiny algorithm on FPGAs using highlevel synthesis (HLS) tools. This method significantly reduced the usage of digital signal processing (DSP) units and flip-flops, achieving remarkable real-time application latency.…”

Section: Introductionmentioning

confidence: 99%

YOLOv7-3D: A Monocular 3D Traffic Object Detection Method from a Roadside Perspective

Ye,

Zhang,

et al. 2023

Applied Sciences

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Current autonomous driving systems predominantly focus on 3D object perception from the vehicle’s perspective. However, the single-camera 3D object detection algorithm in the roadside monitoring scenario provides stereo perception of traffic objects, offering more accurate collection and analysis of traffic information to ensure reliable support for urban traffic safety. In this paper, we propose the YOLOv7-3D algorithm specifically designed for single-camera 3D object detection from a roadside viewpoint. Our approach utilizes various information, including 2D bounding boxes, projected corner keypoints, and offset vectors relative to the center of the 2D bounding boxes, to enhance the accuracy of 3D object bounding box detection. Additionally, we introduce a 5-layer feature pyramid network (FPN) structure and a multi-scale spatial attention mechanism to improve feature saliency for objects of different scales, thereby enhancing the detection accuracy of the network. Experimental results demonstrate that our YOLOv7-3D network achieved significantly higher detection accuracy on the Rope3D dataset while reducing computational complexity by 60%.

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Fast detection and obstacle avoidance on UAVs using lightweight convolutional neural network based on the fusion of radar and camera

Wang,

Zhou

et al. 2024

Appl Intell

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Hardware acceleration of YOLOv7-tiny using high-level synthesis tools

Cited by 10 publications

References 27 publications

Energy-Efficient Implementation of YOLOv8, Instance Segmentation, and Pose Detection on RISC-V SoC

Energy-Efficient Implementation of YOLOv8, Instance Segmentation, and Pose Detection on RISC-V SoC

YOLOv7-3D: A Monocular 3D Traffic Object Detection Method from a Roadside Perspective

Fast detection and obstacle avoidance on UAVs using lightweight convolutional neural network based on the fusion of radar and camera

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