Object Detection in Optical Remote Sensing Images Based on Residual Network

Abstract

Conventional object detection algorithm based on deep learning only make use of deep feature which would become indiscriminative for small target in optical remote sensing images when the network deepens. In this paper, a multi-layer feature fusion method based on residual learning is proposed, which combine the shallow feature with deep feature to classify object comprehensively. First, a ResNet50 backbone network is constructed to extract feature from multiple layers. Then, the scales of feature at different layers are unified through RoI Pooling layer to screen region proposals and be classified through the SVM classifier. Comparative experiments are conducted on UCAS_AOD dataset released by UCAS and the result shows that our model achieved relatively good performance with 0.8802 and 0.9120 f1 score in car and airplane categories respectively.