A Single-stage Multi-class Object Detection Method for Remote Sensing Images

Abstract

Impressive progresses have been achieved in object detection for images by convolution neural networks. However, a robust multi-class object detection method is still one of the great challenges for remote sensing images. Due to the great diversity of scale, orientation, density and background of objects, most advanced object detection algorithms in natural scenes usually suffer a sharp decline in remote sensing images detection. To solve these problems, we proposed a Single-stage Multi-class Object Detection (SMOD) method, aiming at remote sensing images, which can be trained from scratch and detect multi-class objects quickly and precisely. The proposed method introduces a novel Feature Reuse and Attention (FRA) structure as a key module of feature extraction backbone, which combines SE Attention module and dense Feature Reuse connection. Especially, a multiclass detection structure is proposed to learn from multi-scale, multi-level feature map and get effective attention representation for multi-class remote sensing object detection. SMOD can be trained from scratch without pre-trained network stably and converge well simply by employing batch normalization throughout the network. Experiments show that our trainingfrom-scratch method can obtain better performance compared with some state-of-art algorithms on public multi-class remote sensing dataset AIIA2018-6.