基于改进YOLOv4的混凝土裂缝检测方法

Improved YOLOv4-based concrete crack detection method

为了解决深度学习目标检测模型在混凝土裂缝应用上检测精度低、检测速度慢等问题,提出一种基于改进YOLOv4的混凝土裂缝检测方法。首先将YOLOv4的主干特征提取网络替换为轻量级网络Mobilenetv1,并且将YOLOv4加强特征提取网络中的普通标准卷积修改为深度可分离卷积;其次在PANet模块部分添加轻量级注意力模块CBAM(Convolutional Block Attention Module),在控制参数量的基础上提高裂缝目标检测的精度;最后用模拟人类视觉的RFB-s模块代替YOLOv4中的空间金字塔池化模块(Spatial Pyramid Pooling, SPP),扩大感受野,提高检测精度。实验结果表明,与传统YOLOv4相比,本模型的mAP增加三个百分点,参数量减少至14 M,检测速度可达42帧每秒。

A concrete crack detection method based on improved YOLOv4 is proposed to address the problems of low detection accuracy,large number of model parameters and slow detection speed in current deep learning methods for detecting concrete cracks.Firstly,the backbone feature extraction network of YOLOv4 is replaced by the lightweight network Mobilenetv1,and the ordinary standard convolution in the enhanced feature extraction network of YOLOv4 is modified into a depth-separable convolution;secondly,the lightweight attention module CBAM(Convolutional Block Attention Module)in the PANet module to improve the accuracy of crack target detection with a controlled amount of parameters;finally,the Spatial Pyramid Pooling(SPP)module in YOLOv4 is replaced by the RFB-s module that simulates human vision.The experimental results show that compared to conventional YOLOv4,the mAP of this model increases by three percentage points,the amount of parameters is reduced to 14 M and the detection speed is up to 42 frames per second.