Improved Road Damage Detection Algorithm of YOLOv8

被引：10
作者：
Li, Song [1 ]
Shi, Tao [2 ]
Jing, Fangke [1 ]
机构：
[1] School of Electrical Engineering, North China University of Science and Technology, Hebei, Tangshan
[2] School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin
来源：
Computer Engineering and Applications | 2023年 / 59卷 / 23期
关键词：
attention mechanism; deep learning; road damage detection; Transformer; YOLOv8;
D O I：
10.3778/j.issn.1002-8331.2306-0205
中图分类号：
学科分类号：
摘要：
Road damage detection is an important task to ensure road safety and realize timely repair of road damage. Aiming at the problems of low detection efficiency, high cost and difficulty in applying to mobile terminal devices in existing Road Damage detection algorithms, a lightweight road damage detection algorithm YOLOV8-Road Damage （YOLOV8-RD）with improved YOLOv8 is proposed. First, combining the advantages of CNN and Transformer, a BOT module that can extract global and local feature information of road damage images is proposed to adapt to the large-span and elongated features of crack objects. Then, coordinate attention（CA）is introduced in the end of backbone network and neck network to embed the location information into the channel attention, strengthen the feature extraction ability, and suppress the interference of irrelevant features. In addition, C2fGhost module is used in YOLOv8 neck network to reduce floating point computation in feature channel fusion process, reduce the number of model parameters, and improve feature expression performance. The experimental results show that in RDD2022 data set and Road Damage data set, the improved algorithm is 2% and 3.7% higher than the original algorithm compared with mAP50, while the number of model parameters is only 2.8×106 and the computation amount is only 7.3×109, which are reduced by 6.7% and 8.5% respectively. The detection speed of the algorithm reaches 88 FPS, which can accurately detect the road damage target in real time. Compared with other mainstream target detection algorithms, the effectiveness and superiority of this method are verified. © 2023 Chinese Medical Journals Publishing House Co.Ltd. All rights reserved.
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收藏
页码：165 / 174
页数：9
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