Table 4 Comparative study of ODHVCP-HOADL technique with existing methods20,21,36,37,38.

From: Internet of things driven object detection framework for consumer product monitoring using deep transfer learning and hippopotamus optimization

Method

\(\:\varvec{A}\varvec{c}\varvec{c}{\varvec{u}}_{\varvec{y}}\)

\(\:\varvec{P}\varvec{r}\varvec{e}{\varvec{c}}_{\varvec{n}}\)

\(\:\varvec{R}\varvec{e}\varvec{c}{\varvec{a}}_{\varvec{l}}\)

\(\:{\varvec{F}1}_{\varvec{M}\varvec{e}\varvec{a}\varvec{s}\varvec{u}\varvec{r}\varvec{e}}\)

WKNN

96.17

90.79

87.53

89.74

ANN Model

97.14

93.50

77.16

85.96

VoteNet

89.07

90.38

81.78

85.21

VoteNet + 3DRM

95.62

94.80

81.01

88.21

MLCVNet

95.37

92.65

80.36

88.52

YOLOv3

93.64

90.32

87.13

86.89

YOLOv4 + tiny

91.18

91.40

87.92

88.73

YOLOv10

97.80

94.20

77.88

86.58

DCASR

89.73

91.18

82.46

85.96

FSRCNN

96.34

95.08

81.62

88.99

ODHVCP-HOADL

99.64

95.71

88.31

90.98