Abstract Abstract: Traditional deep learning-based Remote sensing image object detection methods faced the challenges of high missed rate and inaccurate classification of dense objects. In order to solve these above problems, an anchor-free deep-learning-based object detection method for cluster objects with rotation in remote sensing image was established. Firstly, CenterNet was used as the baseline network, features were extracted from the backbone network, and an angle regression branch was added on the basis of the original network to perform angle regression. Then, a feature enhancement module based on asymmetric convolution was proposed. The feature map extracted from the backbone network was put into the feature enhancement module to reduce the influence caused by the rotation and turnover of the object, then got the center point and size information of the object. The accuracy of this method on the public dataset is 7.80 percentage points higher than Rotation Region Proposal Networks, and 7.50 percentage points higher than the original CenterNet. On the self-built dataset Ship3, the accuracy is 8.68 percentage points higher than Rotation Region Proposal Networks. And the accuracy of this method is 8.47 percentage points higher than the original CenterNet. Compared with the traditional method, the speed is increased by about 25%, the balance between accuracy and speed is reached.
Received: 31 May 2021
Published: 17 September 2021