Class-Aware Prediction: A Solution to Center Point Collision for Anchor-Free Object Detection in Aerial Images

Object detection in aerial images has emerged as a critically important field in recent years, with anchor-free detectors garnering considerable attention from researchers. However, these detectors often encounter challenges when objects of different classes overlap, leading to false detection due to key point collisions. To solve this problem, we propose a novel method called class-aware prediction, which performs bounding box predictions across all potential categories. However, class-aware prediction requires detectors to perform bounding box regression and prediction at the appropriate class simultaneously, which greatly aggravates the regressing difficulty of branches. Therefore, a Gaussian prediction method is introduced to simplify the regression difficulty by predicting the distribution of parameters with a redesigned negative log-likelihood (NLL) loss. Extensive experiments conducted on the DOTA dataset have demonstrated the efficacy of our proposed method. Our method equipping ResNet-101 obtains 79.49% mAP on the challenging DOTA dataset, achieving the top ranked accuracy among the mainstream single-stage object detectors in the field of aerial image object detection. Addition results on the DIOR-R dataset also show the effectiveness of our method. The results indicate a marked improvement in the detection of certain object classes that are prone to overlap in aerial images, underscoring the significance of our contributions to the field.