改进YOLOv8的道路损伤检测算法

doi:10.3778/j.issn.1002-8331.2306-0205

摘要/Abstract

摘要： 道路损伤检测是保障道路安全、实现道路损伤及时修复的一项重要任务。针对现有的道路损伤检测算法中检测效率低、成本高昂、难以应用于移动终端设备问题，提出了一种改进YOLOv8的轻量型道路损伤检测算法YOLOv8-Road Damage（YOLOv8-RD）。结合CNN和Transformer的优势，提出了一种能够提取道路损伤图像全局特征信息和局部特征信息的BOT模块，以适应裂纹对象的大跨度与细长特征。在骨干网络末端和颈部网络中引入坐标注意力机制（coordinate attention，CA），将位置信息嵌入到通道注意力中，强化特征提取能力，并抑制无关特征的干扰。在YOLOv8颈部网络中使用C2fGhost模块，以减少特征通道融合过程中的浮点运算量，降低模型参数量，同时提高特征表达性能。实验结果表明，在RDD2022数据集和Road Damage数据集上，改进算法与原算法相比mAP50分别提高了2个百分点和3.7个百分点，而模型参数量仅为2.8×106，计算量仅为7.3×109，分别降低了6.7%和8.5%。算法检测速度达到88?FPS，能够实时准确检测道路损伤目标。通过与其他主流目标检测算法比较，验证了该方法的有效性和优越性。

关键词: 道路损伤检测, 深度学习, YOLOv8, 注意力机制, Transformer

Abstract: 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.

Key words: road damage detection, deep learning, YOLOv8, attention mechanism, Transformer

李松, 史涛, 井方科. 改进YOLOv8的道路损伤检测算法[J]. 计算机工程与应用, 2023, 59(23): 165-174.

LI Song, SHI Tao, JING Fangke. Improved Road Damage Detection Algorithm of YOLOv8[J]. Computer Engineering and Applications, 2023, 59(23): 165-174.

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