Reducing False Detections in Aerial Images by Exploiting the Context Information and Centroid Relationship

Abstract

Object detection in aerial images is critical for military and civilian applications. Due to its broad imaging range, an aerial image contains more complex scenes than natural images, which may result in increased confusion between targets and the background. To address this issue, we propose a context-aware and centroid-prior-based object detector for the object detection task in aerial images. Unlike existing object detectors, the proposed model investigates the feasibility of reducing false detections from confusing backgrounds by exploiting both semantic and neighbor contexts. Additionally, a new label assignment algorithm is developed based on the centroid prior assumption to mitigate the localization errors introduced by context information. The experimental results demonstrate that the proposed model outperforms six advanced object detectors on the optical and synthetic aperture radar (SAR) image datasets, indicating the proposed model’s effectiveness.