Vehicle detection in severe weather has always been a difficult task in the environmental perception of intelligent vehicles. This paper proposes a vehicle detection method based on pseudo-visual search and the histogram of oriented gradients (HOG)–local binary pattern (LBP) feature fusion. Using radar detection information, this method can directly extract the region of interest (ROI) of vehicles from infrared images by imitating human vision. Unlike traditional methods, the pseudo-visual search mechanism is independent of complex image processing and environmental interferences, thereby significantly improving the speed and accuracy of ROI extraction. More notably, the ROI extraction process based on pseudo-visual search can reduce image processing by 40%–80%, with an ROI extraction time of only 4 ms, which is far lower than the traditional algorithms. In addition, we used the HOG–LBP fusion feature to train the vehicle classifier, which improves the extraction ability of local and global features of vehicles. The HOG–LBP fusion feature can improve vehicle detection accuracy by 6%–9%, compared to a single feature. Experimental results show that the accuracy of vehicle detection is 92.7%, and the detection speed is 31 fps, which validates the feasibility of the proposed method and effectively improve the vehicle detection performance in severe weather

恶劣天气下的车辆检测一直是智能车辆环境感知中的难点。本文提出了一种基于伪视觉搜索和定向梯度直方图（HOG）-局部二值模式（LBP）特征融合的车辆检测方法。该方法利用雷达检测信息，通过模仿人类视觉，直接从红外图像中提取车辆的感兴趣区域（ROI）。与传统方法不同，伪视觉搜索机制独立于复杂的图像处理和环境干扰，从而显着提高了ROI提取的速度和准确性。更值得注意的是，基于伪视觉搜索的 ROI 提取过程可以减少 40%–80% 的图像处理，ROI 提取时间仅为 4 ms，远低于传统算法。此外，我们使用 HOG-LBP 融合特征来训练车辆分类器，提高了车辆局部和全局特征的提取能力。与单个特征相比，HOG-LBP 融合特征可以将车辆检测精度提高 6%–9%。实验结果表明，车辆检测准确率为92.7%，检测速度为31 fps，验证了该方法的可行性，有效提高了恶劣天气下车辆检测性能