Urban Broken Curb Stone Detection Supported by Deep Learning
Aiming at the problems of multi-scale,similar feature interference and occlusion of broken curb stones in street view images,a CDD-YOLO(convolutional swin transformer deformable decouple-YOLO)model for the detection of broken curb stones on both sides of the urban street is proposed.According to the characteristics of shape and scale diversity of broken curb stones,the features are fused by embedding C3_STR(convolutional swin transformer)module to enhance the perception performance of the model on multi-scale features.For the false detection phenomenon caused by the interference of similar ground objects,by adding the deformable convolution module,the adaptive characteristics of the target region are utilized to improve the discrimination ability of the model to similar ground objects.In order to avoid the problem of inaccurate positioning caused by occlusion,the decoupled detection head structure is introduced to enhance the extraction ability of the model for fuzzy boundary features.It is verified on the self-made data set of broken curb stones in street view,and through the analysis of results,four evaluation indicators,which are precision,recall,F1 and IoU of the method reaches 82.45％,81.22％,81.01％and 80.23％,respectively.The proposed method is significantly superior to other mainstream target detection methods,which verifies its effectiveness and feasibility.
broken curb stone detectionstreet view imagerymulti-scale targetfeature fusiondecoupled detection head
万方数据
维普
