[1]高雪豪,吴建平,韦杰,等.基于增强多尺度融合YOLOv8的道路病害检测算法[J].计算机技术与发展,2024,34(11):140-147.[doi:10.20165/j.cnki.ISSN1673-629X.2024.0217]
　GAO Xue-hao,WU Jian-ping,WEI Jie,et al.Road Disease Detection Algorithm Based on Enhanced Multi-scale Fusion YOLOv8[J].,2024,34(11):140-147.[doi:10.20165/j.cnki.ISSN1673-629X.2024.0217]
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基于增强多尺度融合YOLOv8的道路病害检测算法()

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
34
期数:
2024年11期
页码:
140-147
栏目:
人工智能
出版日期:
2024-11-10

文章信息/Info

Title:
Road Disease Detection Algorithm Based on Enhanced Multi-scale Fusion YOLOv8
文章编号:
1673-629X(2024)11-0140-08
作者:
高雪豪; 吴建平; 韦杰; 何旭鑫; 余咏
云南大学 信息学院,云南 昆明 650504
Author(s):
GAO Xue-hao; WU Jian-ping; WEI Jie; HE Xu-xin; YU Yong
School of Information Science & Engineering,Yunnan University,Kunming 650504,China
关键词:
道路病害检测; 目标检测; YOLOv8; 多尺度融合; 注意力机制
Keywords:
road disease detection; target detection; YOLOv8; multi-scale fusion; attention mechanism
分类号:
TP312
DOI:
10.20165/j.cnki.ISSN1673-629X.2024.0217
摘要:
在资源有限的户外,针对道路病害检测精准度和实时性不高的问题,提出一种多尺度融合的 YOLOv8 的道路病害检测算法。 在主干网络中使用 C2iAFF 多尺度特征融合模块,缓解背景特征与目标特征之间分辨难的问题,提高对细长裂痕目标的检测能力;构造特征融合模块 RFB3×3,对多尺度目标特征信息进行聚合提取,提高特征的表达能力;加入通道注意力机制 SE,让模型学习重要的特征,提高模型的精准度;最后采用更优的归一化 Wasserstein 距离度量损失函数,使用NWD 帮助小尺寸检测物体定位。 实验结果表明,改进后的道路病害检测模型在仅增加 0. 3M 参数量和 0. 4GFLOPs 计算量的情况下,mAP50 提高了 2. 4 百分点,F1-Score 提高了 2. 4 百分点,达到了道路养护工作要求的检测精度和速度。
Abstract:
In the outdoors with limited resources,a multi-scale fusion YOLOv8 road disease detection algorithm is proposed to solve the problem of low accuracy and real-time performance of road disease detection. The C2iAFF multi-scale feature fusion module is used in the backbone network to alleviate the problem of difficult discrimination between background features and target features,and improve the detection of slender crack targets; the feature fusion module RFB3×3 is constructed to aggregate multi-scale target feature information ex-traction to improve the expression ability of features; the channel attention mechanism SE is added to allow the model to learn important features and improve the accuracy of the model; finally,a better normalized Wasserstein distance measurement loss function is used,and NWD is used to help locate small-sized detection objects. Experimental results show that the improved road disease detection model increased mAP50 by 2. 4 percentage points and F1-Score by 2. 4 percentage points while only increasing the number of parameters by 0.3M and the calculation amount by 0. 4GFLOPs,reaching the detection accuracy and speed required for road maintenance work.

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更新日期/Last Update: 2024-11-10