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Research on Complex Environment Adaptation Technology and Its Algorithm for Intelligent Networked Vehicles

Fan Luo

Fan Luo

Secretarial Office of Lincang Transportation BureauLincang, China
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Luo, Fan
 e 
Xun Qiu

Xun Qiu

Durability Team in Department of Components and Materials Testing and EvaluationCAERI, China
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Qiu, Xun
  
17 mar 2025
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Pubblicato online: 17 mar 2025
Ricevuto: 17 ott 2024
Accettato: 26 gen 2025
DOI: https://doi.org/10.2478/amns-2025-0192
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© 2025 Fan Luo et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

Focus module structure
Focus module structure

Figure 2.

Comparison results of different target detection models
Comparison results of different target detection models

Figure 3.

Model training loss curve
Model training loss curve

Figure 4.

The target PR curve in different environments of the YOLOv5 model
The target PR curve in different environments of the YOLOv5 model

Figure 5.

The target PR curve in different environments of the YOLOv5s-CBC model
The target PR curve in different environments of the YOLOv5s-CBC model

Test results of YOLOv5s model on BDD100K dataset

Class Images Labels P/% R/% mAP@0.5/%
All 4000 10399 84.27 83.93 85.05
Car 1000 9321 92.57 90.18 91.95
Truck 1000 254 85.92 81.62 83.69
Pedestrian 1000 372 86.53 87.20 84.33
Cyclist 1000 452 83.85 85.15 82.79

Test results of YOLOv5s-CBC model on BDD100K dataset

Class Images Labels P/% R/% mAP@0.5/%
All 4000 10399 89.42 88.9 90.68
Car 1000 9321 95.61 94.08 93.59
Truck 1000 254 90.92 88.52 87.64
Pedestrian 1000 372 89.43 91.75 89.59
Cyclist 1000 452 86.47 89.61 86.61
Lingua:
Inglese
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1 volte all'anno
Argomenti della rivista:
Scienze biologiche, Scienze della vita, altro, Matematica, Matematica applicata, Matematica generale, Fisica, Fisica, altro
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