Train rolling stock video segmentation and classification for bogie part inspection automation: a deep learning approach

Journal of Engineering and Applied Science

Table 3 Evaluation of the proposed method using mFI

Baseline ethods/datasets	SSD	R-CNN	Fast R-CNN	Faster R-CNN	Yolo v1	Yolo v2	Yolo v2 with skip	Yolo v2 bifold skip	B-MHAC
B-1	0.4215	0.4125	0.3785	0.3329	0.2882	0.2663	0.2156	0.1752	0.1124
B-2	0.4862	0.4598	0.4296	0.3889	0.3389	0.3025	0.2856	0.2531	0.1853
B-3	0.4621	0.4479	0.4129	0.3609	0.3268	0.2939	0.2556	0.2365	0.1722
B-4	0.4468	0.4352	0.4017	0.3569	0.3075	0.2701	0.2356	0.2036	0.1486
B-5	0.5374	0.5206	0.5251	0.5249	0.5161	0.5177	0.5056	0.4852	0.1672
B-6	0.5827	0.5933	0.5873	0.5789	0.5654	0.5215	0.5206	0.4952	0.2379
Average mFI	0.48945	0.4782	0.4558	0.4239	0.3904	0.362	0.3364	0.3081	0.1706