【摘要】 行人检测算法是支撑自动驾驶系统强安全运行的基础性算法。由于遮挡、尺度多变等现实应用场景的限制，现有算法往往考虑构建较为复杂的特征提取结构，缺乏对推理延时和检测召回的并重考量。针对此问题，提出一种面向遮挡等复杂场景的实时鲁棒行人检测算法。设计一种低参数多尺度特征融合模块，以较低计算开销捕获多尺度的行人特征信息；设计一种基于条形感受野的注意力模块，突出特征图中行人的局部相关性，以提升遮挡情况下的推理精度。在拥挤和小尺度场景的实验结果表明，该算法相较于优化前的网络而言，检测精度均有所提高，且网络复杂度和推理延时大幅降低，实现了对遮挡等复杂场景下的低延时高鲁棒性行人检测。更多还原

【Abstract】 Pedestrian detection algorithm is a basic algorithm to support the strong and safe operation of autonomous driving system. Due to the limitations of realistic application scenarios such as occlusion and scale variation, existing algorithms often consider constructing more complex feature extraction structures, and lack of consideration of reasoning delay and detection recall. To solve this problem, this paper proposes a real-time robust pedestrian detection algorithm for complex scenes such as occlusion. A low-parameter multi-scale feature fusion block was designed to capture multi-scale pedestrian feature information with low computational overhead. An attention block based on strip receptive field was designed to highlight the local correlation of pedestrians in the feature map, so as to improve the reasoning accuracy in the case of occlusion. Experimental results in crowded and small-scale scenes show that compared with the network before optimization, the detection accuracy of the proposed algorithm is improved, and the network complexity and deduction delay are greatly reduced, which realizes low delay and high robust pedestrian detection in complex scenes such as occlusion.更多还原