同一场景下超大尺度差异物体的识别和定位方法

近年来，深度学习在物体检测方面取得了非常好的效果和突飞猛进的发展，但在某些特殊场景下，如要求同时检测尺度相差极大的目标物体（相差大于100倍）时，现有的物体识别方法的性能急剧下降。针对同一场景下超大尺度差异物体识别与定位问题，对YOLOv3框架进行了改进，结合图像金字塔技术来提取图像的多尺度特征；并在训练过程中，针对不同尺度的目标提出采用动态交并比（IoU）的策略，此策略可以更好地解决样本不均衡的问题。实验结果表明，该模型对同一场景下超大超小物体的识别能力有了明显的提升。将之应用于机场环境，取得了较好的应用效果。

Recognition and localization method of super-large-scale variance objects in the same scene

In recent years, deep learning achieves very good results and has great improvement in object detection. However, in some special scenes, for example, when it is required to simultaneously detect objects with greatly different scales (difference greater than 100 times), common object recognition methods' performance will drop dramatically. Aiming at the problem of recognizing and locating objects with super-large-scale variance in the same scene, the You Only Look Once version3 (YOLOv3) framework was improved, the image pyramid technology was combined to extract the multi-scale features of the image. And in the training process, the strategy of using dynamic Intersection over Union (IoU) was proposed for different scale objects, which was able to better solve the problem of sample imbalance. Experimental results show that the proposed model significantly improves the recognition ability of super-large and super-small objects in the same scene. The proposed model has been applied to the airport environment and achieved good application results.