Efficient ConvNet Feature Extraction with Multiple RoI Pooling for Landmark-Based Visual Localization of Autonomous Vehicles

Table 3

Localization accuracy comparison of MRoI-FastRCNN-AlexNet versus AlexNet and MRoI-FastRCNN-VGG-M1024 versus VGG-M1024 in terms of maximum precision at 100% recall (Pr. at 100% Re.), maximum recall at 100% precision (Re. at 100% Pr.), and average precision (AP). The highest value with respect to each metric on each dataset is highlighted in bold. The middle italic values are the difference between MRoI-FastRCNN-AlexNet/VGG-M1024 and AlexNet/VGG-M1024.

Method	UACampus			St. Lucia			Nordland			Mapillary
	Pr. at 100% Re.	Re. at 100% Pr.	AP	Pr. at 100% Re.	Re. at 100% Pr.	AP	Pr. at 100% Re.	Re. at 100% Pr.	AP	Pr. at 100% Re.	Re. at 100% Pr.	AP
MRoI-FastRCNN- AlexNet	98.9%	98.6%	99.7%	89.3%	68.2%	98.2%	75.2%	34.0%	92.1%	72.6%	31.2%	90.4%
	+3.4%	+7.3%	+0.3%	+2.7%	+3.1%	+0.7%	+5.7%	+15.4%	+4.6%	−0.8%	−2.2%	−1.1%
AlexNet	95.5%	91.3%	99.4%	86.6%	65.1%	97.5%	69.5%	18.6%	87.5%	73.4%	33.4%	91.5%
MRoI-FastRCNN- VGG-M1024	98.3%	92.0%	99.6%	89.5%	74.4%	98.3%	75.0%	36.7%	92.3%	73.1%	24.3%	90.7%
	+3.8%	+7.0%	+0.5%	+8.2%	+28.1%	+2.6%	+4.5%	+12.2%	+3.5%	−1.2%	−3.6%	−1.3%
VGG-M1024	94.5%	85.0%	99.1%	81.3%	46.3%	95.7%	70.5%	24.5%	88.8%	74.3%	27.9%	92.0%