改进型DSSD算法在道路损伤检测中的应用研究

Research on application of improved DSSD algorithm in road damage detection

在自动检测中,由于道路损伤数据集存在小目标损伤难检测与类别不平衡问题,导致道路损伤检测的准确率低、虚假率高。为此,在DSSD(Deconvolutional Single Shot Detector)网络模型的基础上,提出一种结合注意力机制和Focal loss的道路损伤检测算法。首先,采用识别精度更高的ResNet-101作为DSSD模型的基础网络;其次,在ResNet-101主干网络中添加注意力机制,采用通道域注意力和空间域注意力结合的方式,实现特征在通道维度上的加权与空间维度上的聚焦,提升对小目标道路损伤的检测效果;最后,为了减少简单样本的权重,增大难分类样本的权重,使用Focal loss来提高整体的检测效果。在Global Road Damage Detection Challenge比赛所提供的数据集上进行验证,实验结果表明,该模型的平均精度均值为83.95%,比基于SSD和YOLO网络的道路损伤检测方法的准确率更高。

In the automatic detection,the road damage data set has the problems of difficult detection of small target damage and imbalance of categories,resulting in low accuracy and high false rate of road damage detection.For this reason,based on the DSSD(deconvolutional single shot detector)network model,a road damage detection algorithm combining attention mechanism and Focal loss is proposed.First of all,ResNet-101 with higher recognition accuracy is used as the basic network of the DSSD model.Secondly,an attention mechanism is added to the ResNet-101 backbone network,and the channel domain attention and spatial domain attention are combined to achieve the weighting of features in the channel dimension and the focus on the spatial dimension,and improve the detection effect of small target road damage.Finally,in order to reduce the weight of simple samples and increase the weight of difficult-to-classify samples,Focal loss is used to improve the overall detection effect.It is verified on the data set provided by the Global Road Damage Detection Challenge competition.The experimental results show that the average accuracy of the model is 83.95%,which is more accurate than the road damage detection method based on SSD and YOLO network.