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Table 9 Comparison of PFLO with state-of-the-art pose estimation models

From: PFLO: a high-throughput pose estimation model for field maize based on YOLO architecture

Models

Backbone

Approach

P (%)

R (%)

mAP50 (%)

mAP50 - 95 (%)

Inference time (ms)

HigherHRNet

HRNet-w48

Bottom-up

41.6

39.0

42.1***

14.3***

154

DEKR

Pose_HRNet_w48

Bottom-up

41.0

22.7

38.5***

34.9***

973

YOLOv5x

CSPDarknet

Top-down

71.4

66.2

66.9***

36.7***

18.3

YOLOv6x

CSPDarknet

Top-down

67.2

62.8

61.5***

32.1***

17.5

YOLOv8x

CSPDarknet

Top-down

71.3

66.0

68.1**

38.3*

18.6

YOLOv9e

CSPDarknet

Top-down

72.8

68.9

69.9***

39.8**

16.7

YOLOv10x

CSPDarknet

Top-down

73.3

67.8

69.5*

38.5**

23.7

YOLO11x

CSPDarknet

Top-down

73.8

67.5

69.4***

38.5***

15.9

PFLO

CSPDarknet

Top-down

75.2 ± 0.4

70.2 ± 1.2

72.2 ± 2.6

42.7 ± 3.6

24.7

  1. Bold values indicate the best results in each comparison
  2. *p < 0.05, **p < 0.01, ***p < 0.001 (compared to PFLO, Welch's t-test)