A Deep Neural Network-Based Target Recognition Algorithm for Robot Scenes

Table 3

MS COCO dataset test results.

Methods	Basic network	AP0.5 : 0.95	AP0.5	AP0.75	APS	APM	APL	AR1	AR10	AR100	ARs	ARM	ARL
Faster	VGG16	21.9	42.7	—	—	—	—	—	—	—	—	—	—
ION	VGG16	23.6	43.2	23.6	6.4	24.1	38.3	23.2	32.7	33.2	10.1	37.7	53.6
R-FCN	Residual-101	29.2	51.5	—	10.3	32.4	43.3	—	—	—	—	—	—
DSOD	DS/64/192/4	29.3	47.3	30.6	9.4	31.5	47	27.3	40.7	43	16.7	47.1	65
YOLOv2	Darknet	21.6	44	19.2	9	28.9	41.9	24.8	37.5	39.8	14	43.5	59
SSD300	VGG16	25.1	43.1	25.8	6.6	25.9	41.4	23.7	35.1	37.2	11.2	40.4	58.4
DSSD321	Residual-101	28	46.1	29.2	7.4	28.1	47.6	25.5	37.1	39.4	12.7	42	62.6
STDN321	DenseNet	28	45.6	29.4	7.9	29.7	45.1	24.4	36.1	38.4	12.5	42.7	60.1
Ours320	VGG16	28.2	47.7	29.1	10.3	31.4	43.7	25.8	38.9	41.2	16.9	47.2	61
SSD512	VGG16	28.8	48.5	30.3	10.9	31.8	43.5	26.1	39.5	42	16.5	46.6	60.8
DSSD513	Residual-101	33.2	53.3	35.2	13	35.4	51.1	28.9	43.5	46.2	21.8	49.1	66.4
STDN513	DenseNet	31.8	51	33.6	14.4	36.1	43.4	27	40.1	41.9	18.3	48.3	57.3
Ours512	VGG16	33.1	52.3	32.4	15.6	34.6	42.7	28.3	42.6	45.6	25.9	50.8	60.1

Bold values represent the experimental results of our method.