Probability-Enhanced Anchor-Free Detector for Remote-Sensing Object Detection

Anchor-free object-detection methods achieve a significant advancement in field of computer vision, particularly in the realm of real-time inferences. However, in remote sensing object detection, anchor-free methods often lack of capability in separating the foreground and background. This paper proposes an anchor-free method named probability-enhanced anchor-free detector (ProEnDet) for remote sensing object detection. First, a weighted bidirectional feature pyramid is used for feature extraction. Second, we introduce probability enhancement to strengthen the classification of the object’s foreground and background. The detector uses the logarithm likelihood as the final score to improve the classification of the foreground and background of the object. ProEnDet is verified using the DIOR and NWPU-VHR-10 datasets. The experiment achieved mean average precisions of 61.4 and 69.0 on the DIOR dataset and NWPU-VHR-10 dataset, respectively. ProEnDet achieves a speed of 32.4 FPS on the DIOR dataset, which satisfies the real-time requirements for remote-sensing object detection.