Improved TPH for object detection in aerial images

Abstract

ABSTRACT Deep learning has greatly enhanced the general object detection capability. However, when directly applied to aerial images, performance drops significantly due to: (1) Most objects in aerial images are dense and small; (2) UAV altitude variations cause diverse object scales. In this paper, we improve TPH algorithm for exceptional aerial object detection performance. Specifically, we introduce the SPD module to replace the strided convolution layers and pooling layers. And we improve the TPH backbone and neck networks so that large and small objects can be detected accurately. Experiments on VisDrone2019 and DOTA datasets validate the effectiveness of our method.