Table 1 Segmentation results of YuGong, compared to state-of-the-art methods on the AutoMine dataset² (a mine dataset)

SegFormer⁵¹

99.8

84.83

62.98

88.86

68.96

90.05

Mask2Former⁵²

99.81

86.46

63.58

88.65

70.84

89.22

Swin⁵³

99.74

82.41

54.5

86.37

63.82

80.85

FCN⁵⁴

99.69

79.78

29.48

86.23

62.03

74.16

Segnext⁵⁵

99.55

81.22

56.1

86.55

65.4

69.2

Segmenter⁵⁶

98.81

74.63

49.6

78.23

56

76.09

UPerNet⁵⁷

99.72

80.51

12.04

86.03

64.86

81.37

deeplabv3+⁵⁸

98.9

79.19

50.96

83.88

63.46

73.81

U-Net⁵⁹

99.43

74.42

35.22

83.99

54.31

68.75

YuGong

99.86

92.67

64.24

95.59

81.46

93.68