目的 行人检测是目标检测中的一个基准问题，在自动驾驶等场景有着较大的实用价值，在路径规划和智能避障方面发挥着重要作用。受限于现实的算法功耗和运行效率，在自动驾驶场景下行人检测存在检测速度不佳、遮挡行人检测精度不足和小尺度行人漏检率高等问题，在保证实时性的前提下设计一种适合行人检测的算法，是一项挑战性的工作。方法 本文旨在解决自动驾驶场景中耗时长、行人遮挡和小尺度行人检测结果精度低的问题，提出了一种尺度注意力并行检测算法（scale-aware and efficient object detection，Scale-aware EfficientDet）：在特征提取与检测中使用了EfficientDet的主干网络，保证算法效率和功耗的平衡；在行人遮挡方面，为了提高模型对遮挡现象的检测精度，引入了可以增强行人与其他物体之间特征差异的损失函数；在提高小目标行人检测精度方面，采用scale-aware双路网络算法来增加对小目标行人的检测精度。结果 本文选择Caltech行人数据集作为对比数据集，选取YOLO（you only look once）、YOLOv3、SA-FastRCNN（scale-aware fast region-based convolutional neural network）等算法进行对比，在运行效率方面，本文算法在连续输入单帧图像的情况下达到了35帧/s，多图像输入时达到了70帧/s的工作效率；在模型精度测试中，本文算法也略胜一筹。本文算法应用于2020年中国智能汽车大赛中，在安全避障环节皆获得满分。结论 本文设计的尺度感知的行人检测算法，在EfficientDet高性能检测器的基础上，通过结合损失函数、scale-aware双路子网络的改进，进一步提升了本文检测器的鲁棒性。

Objective Pedestrian detection is a crucial safety factor in autonomous driving scenarios. Consistent pedestrian detection results play a particular role in path planning and pedestrian collision avoidance. In recent years, pedestrian detection algorithms have become a research hotspot in the field of autonomous driving. For the pedestrian detection task, several problems need to be solved. 1) Pedestrian occlusion in traffic scenes. Pedestrian occlusion is a challenging driving safety problem in autonomous driving scenarios. Pedestrians who are obscured by other objects (such as buildings, vehicles, and other pedestrians) are difficult to detect. 2) Small pedestrian detection accuracy needs to be improved. In an autonomous driving environment, the accuracy of pedestrian detection plays a crucial role in vehicle control systems based on vision algorithms. When the vehicle speed is fast, the pedestrians at a long distance need to be detected accurately. With the need for low algorithm power consumption and good operating efficiency, designing an algorithm suitable for pedestrian detection to maintain excellent detection performance under the premise of achieving real-time performance is a difficult problem. Method This paper proposed a real-time pedestrian detection algorithm called scale-aware and efficient object detection (Scale-aware EfficientDet) based on EfficientDet, which achieves state-of-the-art performance in object detection. Our approach aimed to solve the problems of high time consumption, pedestrian occlusion, and low accuracy of small pedestrian detection results in autonomous driving scenarios. Most of the computing power and running time of the existing object detection algorithms are consumed in the visual feature extraction stage, so the use of a lightweight feature extraction network is a crucial factor in improving the efficiency of the algorithm. Our method uses EfficientDet in feature extraction to ensure the algorithm's computational efficiency and power consumption balance. Our approach aimed to observe occluded pedestrians precisely. The loss function was introduced to improve the model's detection accuracy of occlusion phenomena. The function can enhance the feature difference between pedestrians and other objects, and reduce the feature difference between occlude pedestrians and normal pedestrians. In terms of improving the accuracy of small target pedestrian detection, we use the scale-aware mechanism to enhance the algorithm's detection accuracy for small target pedestrians. Result The Caltech pedestrian dataset was used for model comparison. You only look once (YOLO), YOLOv3 scale-aware fast region-based convolutional neural network (fast R-CNN), and other algorithms are selected for comparison. In terms of operating efficiency, our algorithm achieves 35 frame/s with continuous input of a single frame image and a working efficiency of 70 frame/s with multi-image input. In the test of model accuracy, our algorithm is more accurate than YOLOv3, SA-FastRCNN(scale-aware fast region-based convolutional neural network), EfficientDet, and other algorithms. In the preliminaries and finals of the China Intelligent Vehicle Championship(CIVC) 2020, the safety and obstacle avoidance links all received full marks. Conclusion To address the problems of detection speed in pedestrian detection in autonomous driving, this paper designs the scale-aware EfficientDet real-time pedestrian detector, which is based on the efficient and high-precision EfficientDet. Our method solved the insufficient detection accuracy for occluded pedestrians and the high missed detection rate of small-scale pedestrians. In accordance with the occlusion characteristics of pedestrians, the loss function with repulsive force is used to solve the problem of pedestrian occlusion. Considering the significant differences in visual appearance and extracted feature maps between small-scale and large-scale pedestrians, scale-aware networks are used separately to minimize the missed detection rate of small-scale pedestrians. The improvements in these two aspects further improve the robustness of the designed detector. In future work, our methods can be adjusted to improve detection performance, find optimization methods, and improve neural networks. The detection performance and detection accuracy can be further improved to promote its better application in the field of autonomous driving.