基于YOLOv5-MobileNetV3算法的目标检测
Object Detection Based on YOLOv5-MobileNetV3 Algorithm
曲英伟 1刘锐1
作者信息
- 1. 大连交通大学软件学院,大连 116052
- 折叠
摘要
车辆行驶过程中,对前方目标的检测速度和检测精度一直是自动驾驶领域研究的重点.针对现有的目标检测算法模型,在复杂交通环境下,传统模型面对重叠目标容易导致误检和漏检的情况发生,大幅度提高检测精度又会造成计算量过大导致处理速度缓慢,实时性下降的问题.本文提出基于YOLOv5 模型的改进算法.首先采用MobileNetV3 网络替换原模型中主干网络C3 的方案,实现网络仍保持轻量化的同时,提高模型响应速度.其次,提出一种非极大值抑制算法Adaptive-EIoU-NMS来提高重叠目标的识别精度.最后采用K-means++聚类算法替换原有聚类算法,生成更精确的锚框.实验结果表明,改进后的模型平均检测精度达到 90.1%,检测速度达到89 f/s.实验结果可以证实,改进后的模型针对复杂场景检测精度和检测速度都有显著提高.
Abstract
The detection speed and accuracy of detecting targets ahead during vehicle operation have always been a focus of research in the field of autonomous driving.For existing object detection algorithm models,in complex traffic environments,traditional models are prone to false positives and missed detections when facing overlapping targets.Significantly improving detection accuracy can also lead to increased computational demands,resulting in slower processing speed and decreased real-time performance.This article proposes an improved algorithm based on the YOLOv5 model.Firstly,the MobileNetV3 network is adopted to replace the C3 backbone network in the original model,achieving a lightweight network while improving the model's response speed.Secondly,a non-maximum suppression algorithm,Adaptive-EIoU-NMS,is proposed to improve the recognition accuracy of overlapping targets.Finally,the K-means++clustering algorithm is used to replace the original clustering algorithm and generate more accurate anchor boxes.Experimental results show that the improved model achieves an average detection accuracy of 90.1%and a detection speed of 89 frames per second(f/s).The experimental results confirm that the enhanced model significantly improves both detection accuracy and speed for complex scene detection.
关键词
自动驾驶/YOLOv5/MobileNetV3/Adaptive-EIoU-NMS/K-means++Key words
autonomous driving/YOLOv5/MobileNetV3/Adaptive-EIoU-NMS/K-means++引用本文复制引用
出版年
2024
NETL
NSTL
万方数据