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, which amplifies the disparities between… More >
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