Task-Specific Heterogeneous Network for Object Detection in Aerial Images

Object detection in aerial images has attracted increasing attention in recent years. Due to the complex background and arbitrary-oriented objects, it is challenging to accurately locate the objects of interest in the images. Many methods have been developed for improving localization accuracy of oriented objects. However, classification and localization tasks require different features due to the unique characteristics of aerial images, which are still not fully considered in previous methods. Therefore, we propose a task-specific heterogeneous (TSH) network for aerial object detection. Specifically, we design an interference-suppression module (ISM) to reduce both the background and interclass interference, which can provide discriminative features for classification. To produce more reliable localization confidence, we propose a joint-learning quality estimation (JQE) module to adaptively combine the classification and regression features, thereby achieving accurate classification and localization quality estimation simultaneously. Moreover, we propose a point-based localization (PBL) branch. In the PBL, the learnable points can effectively adapt to objects with diverse shapes and orientations, and the dynamic information aggregation (DIA) module can enhance the relationships between the dispersible points to promote localization accuracy. The proposed TSH is evaluated extensively on four widely used aerial datasets, demonstrating its state-of-the-art performance. Ablation study and visualizations further verify the effectiveness of our method.