Glass Recognition and Map Optimization Method for Mobile Robot Based on Boundary Guidance

Chinese Journal of Mechanical Engineering

Table 1 Comparison of test results for GDD

Method	CRF	IoU↑	Acc↑	F-beta↑	mAE↓	BER↓
PSPNet	−	84.06	0.906	0.906	0.084	8.79
DenseASPP	−	83.68	0.919	0.911	0.081	8.66
DANet	−	84.15	0.911	0.901	0.089	8.96
CCNet	−	84.29	0.915	0.904	0.085	8.63
PointRend	−	86.51	0.933	0.928	0.067	6.50
DSS	−	80.24	0.898	0.890	0.123	9.73
PiCANet	−	83.73	0.916	0.909	0.093	8.26
BASNet	−	82.88	0.907	0.896	0.094	8.70
EGNet	−	85.04	0.920	0.916	0.083	7.43
DSC	−	83.56	0.914	0.911	0.090	7.97
BDRAR	+	80.01	0.902	0.908	0.098	9.87
GDNet (RNeXt101)	+	87.63	0.939	0.937	0.063	5.62
EBLNet (RNet101)	−	88.16	0.941	0.939	0.059	5.58
EBLNet (RNeXt101)	−	88.72	0.944	0.940	0.055	5.36
EBLCNet (ResNet101) 1C	−	88.38	0.939	0.945	0.061	6.01
EBLCNet (ResNet101) 2C	−	88.71	0.941	0.941	0.059	5.76
EBLCNet (ResNet101) 3C	−	88.68	0.941	0.941	0.059	5.86