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.
关键词
破损路沿石检测/街景影像/目标多尺度/特征融合/解耦检测头
Key words
broken curb stone detection/street view imagery/multi-scale target/feature fusion/decoupled detection head
维普
万方数据