基于anchor-free的交通场景目标检测技术

在智能交通领域使用深度学习的方法进行目标检测已成为研究热点。当下经典的目标检测算法,无论是基于回归的单阶目标检测模型还是基于候选区域的二阶段目标检测模型,大部分是利用大量预定义的先验框anchor枚举可能的位置、尺寸和纵横比的方法来搜索对象,往往会造成正负样本严重不均衡的问题,模型的性能和泛化能力也受到anchor自身设计的限制。针对基于anchor的目标检测算法存在的问题,利用单阶目标检测网络RetinaNet,对交通场景中的车辆、行人和骑行者建立基于anchor-free的目标检测模型,采用逐像素预测的方式处理目标检测问题,并添加中心性预测分支,提升检测性能。实验表明,与基于anchor的原RetinaNet算法相比,改进的基于anchor-free的目标检测模型算法能够对交通场景中的车辆、行人、骑行者实现更好的识别。

Object detection in traffic scenes based on anchor-free

Object detection using deep learning methods in the field of intelligent transportation has become a research hotspot. Currently, most of the classic object detection algorithms, whether which are the single-stage object detection models based on regression or the two-stage object detection models based on candidate regions, use a large number of predefined priori boxes called “anchor” to enumerate the possible positions, sizes and aspect ratios so as to search the objects. It will cause serious imbalance between positive and negative samples, and the performance and generalization ability of the models are also limited by the anchor's design. Aiming at the above problems of the anchor-based object detection algorithms, a single-stage object detection network, called RetinaNet, is used to establish the anchor-free based object detection models for vehicles, pedestrians, and cyclists in traffic scenes. Pixel-by-pixel prediction is adopted to handle object detection and add central prediction branches to improve the detection performance. Experiments show that, compared with the original RetinaNet algorithm based on anchor, the improved algorithm can better recognize vehicles, pedestrians, and cyclists in traffic scenes.