面向智能建筑现场实时目标检测的YOLOv4模型推理加速研究

Jianchun Wang, Minjian Long, Yunfu Zhou, Congcong Guan
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引用次数: 0

摘要

在智能施工现场,往往需要同时检测实时监控流中的多个对象。如果不加速YOLOv4模型,就会产生较高的推理延迟,无法达到实时检测的目的。本文首先介绍了YOLOv4模型的特点,然后详细讨论了如何使用YOLOv4-tiny- 31和TensorRT来加速YOLOv4模型的推理过程。实验表明,YOLOv4-tiny-3l模型可以平滑地检测多实时流中的目标,但精度较差,不能用于实际应用。采用TensorRT工具箱对具有FP16精度的YOLOv4模型进行量化时,加速模型可以平滑地检测多个实时流中的目标,并且精度损失很小。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on YOLOv4 model inference acceleration of real time object detection for smart construction site
In smart construction site, multi objects in real time monitoring streams are often needed to be detected at the same time. If YOLOv4 models are not accelerated, higher inference delay will be occurred, so that the purpose of real time detection can’t be achieved. The features of YOLOv4 model are firstly introduced in this paper, and then we discuss how to use YOLOv4-tiny-3l and TensorRT to accelerate the inference process of YOLOv4 model in detail. The experiments show that YOLOv4-tiny-3l models can be used to detection objects in multi real time streams smoothly, but the accuracy is pretty poor, so that the models can’t be used in practices. When adopting TensorRT toolkit to quantize YOLOv4 models with FP16 precision, the accelerated models can be used to detect objects in multi real time streams smoothly with a small loss of accuracy.
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