Acceso abierto

Optimisation of highway vehicle occlusion recognition based on attention and multitasking approach

Shifeng Feng

Shifeng Feng

Hebei Expressway Group Limited Qingyin BranchShijiazhuang, China
Buscar este autor en
Sciendo|Google Scholar
Feng, Shifeng
  
17 mar 2025
Optimisation of highway vehicle occlusion recognition based on attention and multitasking approach's Cover Image
Acerca de este artículo
Artículo anterior
Artículo siguiente
Cite
Descargar portada
Publicado en línea: 17 mar 2025
Recibido: 18 oct 2024
Aceptado: 12 feb 2025
DOI: https://doi.org/10.2478/amns-2025-0180
Palabras clave
© 2025 Shifeng Feng, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

Key point extraction and coordinate positioning of vehicles
Key point extraction and coordinate positioning of vehicles

Figure 2.

The structure of CAMTS
The structure of CAMTS

Figure 3.

Structure of two line multi-task vehicle recognition model
Structure of two line multi-task vehicle recognition model

Figure 4.

The structure of MAFF
The structure of MAFF

Figure 5.

A two-line multitasking vehicle occlusion recognition modeling process
A two-line multitasking vehicle occlusion recognition modeling process

Figure 6.

Ablation test results
Ablation test results

Figure 7.

PR curves and area test results for each module
PR curves and area test results for each module

Figure 8.

The occlusion detection results of different detection methods
The occlusion detection results of different detection methods

Comparison of different methods for detecting occluded vehicles

Data set Model P/% R/% F1/% MSE MAE
COCO Mask R-CNN 79.33 82.29 80.81 0.04 0.06
Faster R-CNN 84.69 84.17 84.43 0.02 0.04
YOLOv7 89.75 88.74 89.32 0.02 0.02
Our method 93.28 92.47 92.82 0.01 0.02
ApolloScape Mask R-CNN 84.25 86.54 85.41 0.03 0.06
Faster R-CNN 86.69 88.15 87.36 0.03 0.05
YOLOv7 89.51 90.33 89.51 0.03 0.04
Our method 92.23 93.82 92.62 0.01 0.02

Comparison of model detection results under different occlusion conditions

Occlusion rate Method Color precision/% Vehicle type precision/% Detection time/s
20% Mask R-CNN 79.08 87.22 1.04
Faster R-CNN 76.33 72.23 1.46
YOLOv7 82.81 73.52 1.22
Our method 91.74 93.49 0.29
40% Mask R-CNN 80.13 79.45 0.93
Faster R-CNN 75.62 86.51 0.77
YOLOv7 82.77 89.52 0.61
Our method 90.06 89.06 0.44
60% Mask R-CNN 80.45 80.94 0.55
Faster R-CNN 83.92 78.25 1.94
YOLOv7 76.76 83.49 0.74
Our method 94.56 88.67 0.21
80% Mask R-CNN 78.77 81.47 1.28
Faster R-CNN 82.36 84.22 1.18
YOLOv7 84.58 87.11 1.92
Our method 92.52 88.16 0.53
Idioma:
Inglés
Calendario de la edición:
1 veces al año
Temas de la revista:
Ciencias de la vida, Ciencias de la vida, otros, Matemáticas, Matemáticas aplicadas, Matemáticas generales, Física, Física, otros
RSS Feed de revista