Tianmin Deng*, Xiyue Zhang, Xinxin Cheng
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 533-549, 2024, DOI:10.32604/cmc.2023.044639
Abstract Vehicle detection plays a crucial role in the field of autonomous driving technology. However, directly applying deep learning-based object detection algorithms to complex road scene images often leads to subpar performance and slow inference speeds in vehicle detection. Achieving a balance between accuracy and detection speed is crucial for real-time object detection in real-world road scenes. This paper proposes a high-precision and fast vehicle detector called the feature-guided bidirectional pyramid network (FBPN). Firstly, to tackle challenges like vehicle occlusion and significant background interference, the efficient feature filtering module (EFFM) is introduced into the deep network,… More >
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