科技创新与应用2024，Vol.14Issue(21)：56-60,5.DOI:10.19981/j.CN23-1581/G3.2024.21.014
基于改进YOLOv8的道路坑洼检测算法
摘要
Abstract
In order to solve the problem of low accuracy of existing object detection algorithms in road pothole detection,a road pothole detection algorithm based on improved YOLOv8 is proposed.First of all,the Triplet Attention(TA)module is introduced into the YOLOv8 backbone network to emphasize the importance of capturing cross-dimensional interactions when calculating attention weights,so as to provide richer feature representations and be more efficient in calculation,which is helpful to locate and identify detection objects more accurately.In this study,a new lightweight detection head,Flex_Detect,is proposed for potholed roads,which uses double-branch convolution and dynamically adjusts the anchor frame to ensure that the model can effectively detect targets on feature maps of different scales,which is helpful to improve the adaptability of the model to targets of different sizes,and improve the performance and generalization ability of the model in object detection tasks.The experimental results show that the average accuracy of YOLOv8_Efficient on open data sets is 2.5％higher than that of the original YOLOv8n and 4.1％higher than that of YOLOv5n.关键词
目标检测/注意力机制/检测头/道路坑洼检测/YOLOv8Key words
object detection/attention mechanism/detection head/road pothole detection/YOLOv8分类
信息技术与安全科学引用本文复制引用
白瑞瑞,赵建光,赵佳娜,郑志豪..基于改进YOLOv8的道路坑洼检测算法[J].科技创新与应用,2024,14(21):56-60,5.基金项目
河北省教育厅科学研究项目(QN2024148) （QN2024148）
