An Improved Object Detection CNN Module for Remote Sensing Images

Convolutional neural network (CNN)-based object detection methods have aroused widespread interest in the remote sensing images field, which usually achieve satisfactory results. However, there still exist some factors that cause the detection performance to degrade, such as scales variability, back-ground complexity and objects tininess. In this work, we propose a CNN module that combines semantic information with fine-grained information and can replace the basic block in the backbone of object detection methods to enhance performance. More specifically, our module include a double branches for extracting semantic information and fine-grained information, and an Efficient channel attention (ECA) module for adjusting weights in channel-wise. Experimental results on DIOR dataset suggest the superiority of our module.