RAOD: refined oriented detector with augmented feature in remote sensing images object detection

Abstract

Object detection is a challenging task in remote sensing. Aerial images are distinguished by complex backgrounds, arbitrary orientations, and dense distributions. Considering those difficulties, this paper proposes a two-stage refined oriented detector with augmented features named RAOD. First, a novel Augmented Feature Pyramid Network (A-FPN) is built to enhance fusion both in spatial and channel dimensions. Specifically, it mainly consists of three modules: Scale Transfer Module (STM), Feature Aggregate Module (FAM) and Feature Refinement Module (FRM). STM reduces information loss when fusing features in the top-down pathway. FAM aggregates features from different scales. FRM aims to refine the integrated features using a lightweight attention module. Then, we adopt a two-step processing, which consists of a coarse stage and a refinement stage. In the coarse stage, deformable RoI pooling is adopted to improve the network’s ability of modeling spatial transformations and then horizontal proposals are transformed into oriented ones. In the refinement stage, Rotated RoI align (RRoI align) is used to extract rotation-invariant features from rotated RoIs and further optimize the localization. To enhance stability and robustness during training, smooth Ln is chosen as regression loss as it has better ability in terms of robustness and stability than smooth L1 loss. Extensive experiments on several rotation detection datasets demonstrate the effectiveness of our method. Results show that our method is able to achieve 79.78%, 74.7% and 94.82% on DOTA-v1.0, DOTA-v1.5 and HRSC2016, respectively.

Qin Shi 施秦

Master.

A master student of this laboratory, research interests include Artificial Intelligence, Text Image Processing and Image Super-resolution.

Yu Zhu 朱煜

Professor. Experts in artificial intelligence and computer vision. Lab leader.

Leader of this laboratory, research interests include Artificial Intelligence, Computer Vision, Industrial controls, Digital Image and Video Processing, Machine learning, Deep Learning and Applications.

Chuantao Fang 方传涛

Master. Huge fan of Arsenal ⚽️ team.

A Master student of this laboratory, research interests include Deep Learning, Image Super-Resolution, and Generative Adversarial Nets.