Dynamic Image Location and Recognition Based on YOLOv7

XU Li-bing

doi:10.3969/j.issn.2096-6091.2023.23.001

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Dynamic Image Location and Recognition Based on YOLOv7

RESEARCH PAPER | 更新时间：2025-03-26

- Dynamic Image Location and Recognition Based on YOLOv7
- New Generation of Information Technology Vol. 6, Issue 23, Pages: 1-8(2023)
- 作者机构：
  
  无锡九方科技有限公司，江苏无锡 214011
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.2096-6091.2023.23.001
  CLC： TP391.4
- Published：15 December 2023
- 稿件说明：
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许立兵. 基于YOLOv7的动态图像定位与识别[J]. 新一代信息技术, 2023, 6(23): 01-08

XU Li-bing. Dynamic Image Location and Recognition Based on YOLOv7[J]. New Generation of Information Technology, 2023, 6(23): 01-08
许立兵. 基于YOLOv7的动态图像定位与识别[J]. 新一代信息技术, 2023, 6(23): 01-08 DOI： 10.3969/j.issn.2096-6091.2023.23.001.

XU Li-bing. Dynamic Image Location and Recognition Based on YOLOv7[J]. New Generation of Information Technology, 2023, 6(23): 01-08 DOI： 10.3969/j.issn.2096-6091.2023.23.001.

摘要

目前行人检测技术已趋于成熟，但动态图像检测依然是难点，尤其是在动态图像中需要快速对图像定位与识别的技术。针对动态图像定位与识别困难的问题，本文提出了一种基于YOLOv7的快速定位与识别方法。该方法不同于其他实时目标检测方法集中在高效体系结构设计的思路，YOLOv7将优化重点放在训练过程优化上，通过引入bag-of-freebies模块训练方法，采用增加训练成本但不增加推理成本的方式来提高目标定位与识别的准确性。实验表明，该方法能有效减少实时目标检测器40%左右的参数和50%的计算量，对于动态图像有更好的定位与识别效果。

Abstract

Despite pedestrian detection technology has become mature

dynamic image detection is still difficult

especially in the dynamic image

which needs fast image positioning and recognition. Aiming at the difficulty of dynamic image location and recognition

this paper proposed a fast location and recognition method based on YOLOv7. This method is different from other real-time target detection methods

which mainly focus on the efficient architecture design. YOLOv7 focuses on the optimization of the training process

and improves the accuracy of target location and recognition by introducing the bag-of-freebies module training method

which increases the training cost but does not increase the reasoning cost. The experiment shows that the method can effectively reduce the parameters of the real-time target detector by about 40% and the calculation amount by 50%

and has better positioning and recognition effect for dynamic images.

关键词

Keywords

references

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