工业控制计算机2024,Vol.37Issue(9) :64-66.

改进YOLOv7的复杂驾驶环境下目标检测算法研究

Improved Object Detection Algorithm for YOLOv7 in Complex Driving Environments

王法中 娄莉
工业控制计算机2024,Vol.37Issue(9) :64-66.

改进YOLOv7的复杂驾驶环境下目标检测算法研究

Improved Object Detection Algorithm for YOLOv7 in Complex Driving Environments

王法中 1娄莉1
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作者信息

  • 1. 西安石油大学计算机学院,陕西 西安 710065
  • 折叠

摘要

针对现有目标检测算法对于目标遮挡以及小目标造成的误检漏检等问题,以YOLOv7 作为基础框架,提出了一种复杂驾驶环境下目标检测的改进方法.首先在原模型上引入CBAM注意力机制,抑制背景干扰信息;使用NWD作为算法的损失函数,增强对小目标的检测能力.实验结果表明,在公开数据集KITTI上,相较原算法准确率提高了 4.5%,有效提升了驾驶环境中目标检测的精度.

Abstract

In order to solve the problems of false detection and missed detection caused by the existing object detec-tion algorithms,an improved object detection method in complex driving environment is proposed based on YOLOv7.Firstly,CBAM attention mechanism is introduced into the original model to suppress background interference information.Secondly,NWD is used as the loss function of the algorithm to enhance the detection ability of small object.Experimental results show that,on the public data set KITTI,the accuracy rate is improved by 4.5%compared with the original algorithm.

关键词

目标检测/YOLOv7/注意力机制/深度学习

Key words

object detection/YOLOv7/attention mechanism/deep learning

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出版年

2024
工业控制计算机
中国计算机学会工业控制计算机专业委员会 江苏省计算技术研究所有限责任公司

工业控制计算机

影响因子:0.258
ISSN:1001-182X
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