Multi-channel high-resolution network and attention mechanism fusion for vehicle detection model

doi:10.3969/j.issn.1674-8484.2022.01.012

Abstract

Abstract:

A vehicle detection model named MCHRANet based on the multi-channel high-resolution network and attention mechanism was proposed to improve the safe, reliable, and stable operation of road traffic monitoring and intelligent connected transportation system (ICTS) in the roadside edge platform. The model adopted multi-channel high-resolution network structure design to retain high-resolution features and ensured detection robustness. The feature fusion method incorporating the attention mechanism realized the deep fusion of multi-scale features through the self-learning of feature connection weights. Each channel network adopted jump connections to promote cross-layer feature fusion and accelerate network convergence. A public data set was used to evaluate the vehicle detection performances in different scenarios. The results show that the vehicle detection performance of the proposed model is better than the three traditional models, the improved network recognition mean average precision (AP) index is close to 95%, and it has good robustness for detection in different scenarios.

Key words: vehicle detection, high-resolution network, attention mechanism, feature fusion, jump connection

CLC Number:

TP391.41

DING Fei, MI Guanyu, TONG En, ZHANG Nan, BAO Jianmin, ZHANG Dengyin. Multi-channel high-resolution network and attention mechanism fusion for vehicle detection model[J]. Journal of Automotive Safety and Energy, 2022, 13(1): 122-130.

Figures/Tables 17

References 24

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类别	实例数量
类别	训练集	测试集
轿车	49 940	12 630
卡车	3 043	776
总量	52 983	13 406

类别	实例数量
类别	训练集	测试集
轿车	49 940	12 630
卡车	3 043	776
总量	52 983	13 406