DB-YOLO:特征增强融合的双骨干YOLOv8道路缺陷检测模型

DB-YOLO:Dual Backbone YOLOv8 Model with Feature Enhancement Fusion for Road Defect Detection

虽然已经提出了许多基于深度学习的道路缺陷检测方法,但这些方法通常忽略了一些道路缺陷检测任务中非常重要的道路缺陷相关的边缘特征信息。为了充分利用这些边缘特征信息,提出了一种改进的双骨干YOLOv8模型(dual backbone YOLOv8 model,DB-YOLO)用于道路缺陷检测。设计了边缘特征提取模块(edge feature extraction model,EFEM)用于过滤图像低频信息,提取图像高频边缘信息。设计了双骨干网络来提取特征,在原模型基础上增加一个边缘特征骨干网络(edge feature backbone,EFB),对EFEM提取的图像高频边缘信息进行处理,提取边缘特征,为道路缺陷检测提供更丰富的特征。提出了一种新的特征增强融合模块(feature enhancement fusion module,FEFM)用于融合各种特征,并采用多个FEFM模块将边缘特征、不同级别的图像特征进行有机融合。引入Label smoothing策略减弱了数据集中标签质量的影响,增强了模型的泛化能力,进一步提升模型的检测精度。实验结果表明,在GRDDC2020数据集上,DB-YOLO_v8s的mAP和F1分别取得56.42%、56.13%,较YOLO_v8s分别提升了1.3和1.96个百分点,检测速度达到了64.94帧/s,满足实时检测要求。此外,DB-YOLO_v8s在官方测试集Test_1和Test_2上的F1分数分别为58.79%和58.52%,与其他方法相比,在两个测试数据集中F1分别高了0.65和1.37个百分点。因此,提出的模型可以提升道路缺陷检测精度。

Although many deep learning-based road defect detection methods have been proposed,these methods usually ignore some road defect-related edge feature information of edge-related features that are very important in road defect detection tasks.In order to make full use of this high-frequency information,this paper proposes an improved dual back-bone YOLOv8 model(DB-YOLO)for road defect detection.Firstly,an edge feature extraction model(EFEM)is designed to filter the low-frequency information of the image and extract the high-frequency edge information of the image.Sec-ondly,a dual backbone network is designed to extract features.An edge feature backbone(EFB)is added to the original model to process the high-frequency edge information of the image extracted by EFEM,extract edge features,and provide richer features for road defect detection.Finally,a new feature enhancement fusion module(FEFM)is proposed to fuse various features,and multiple FEFM modules are used to organically fuse edge features and image features of different levels.In addition,the introduction of label smoothing strategy weakens the impact of label quality in the dataset,enhances the generalization ability of the model,and further improves the detection accuracy of the model.Experimental results show that on the GRDDC2020 dataset,the mAP and F1 of DB-YOLO_v8s have achieved 56.42%and 56.13%respectively,which are improved by 1.3 and 1.96 percentage points respectively compared with YOLO_v8s.The detection speed reaches 64.94 frames per second,meeting the real-time detection requirements.In addition,the F1 scores of DB-YOLO_v8s on the official test sets Test_1 and Test_2 are 58.79%and 58.52%respectively.Compared with other methods,the F1 scores in the two test data sets are 0.65 and 1.37 percentage points higher respectively.Therefore,the proposed model can improve road defect detection accuracy.