摘要
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 Efficient Det):在特征提取与检测中使用了Efficient Det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Efficient Det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 Efficient Det) based on Efficient Det,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 Efficient Det in feature extraction to ensure the algorithm’s computational efficiency and power consumption balance. Our approach aimed to
作者
徐歆恺
马岩
钱旭
张龑
Xu Xinkai;Ma Yan;Qian Xu;Zhang Yan(China University of Mining and Technology(Beijing),Beijing 100083,China;Beijing Engineering Research Center of Smart Mechanical Innovation Design Service,Beijing Union University,Beijing 100101,China)
出处
《中国图象图形学报》
CSCD
北大核心
2021年第1期93-100,共8页
Journal of Image and Graphics
基金
国家自然科学基金重点项目(61932012)
北京联合大学科研项目(ZK80202003)。
