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.

Significant advancements have been achieved in road surface extraction based on high-resolution remote sensing image processing. Most current methods rely on fully supervised learning, which necessitates enormous human effort to label the image. Within this field, other research endeavors utilize weakly supervised methods. These approaches aim to reduce the expenses associated with annotation by leveraging sparsely annotated data, such as scribbles. This paper presents a novel technique called a weakly supervised network using scribble-supervised and edge-mask (WSSE-net). This network is a three-branch network architecture, whereby each branch is equipped with a distinct decoder module dedicated to road extraction tasks. One of the branches is dedicated to generating edge masks using edge detection algorithms and optimizing road edge details. The other two branches supervise the model’s training by employing scribble labels and spreading scribble information throughout the image. To address the historical flaw that created pseudo-labels that are not updated with network training, we use mixup to blend prediction results dynamically and continually update new pseudo-labels to steer network training. Our solution demonstrates efficient operation by simultaneously considering both edge-mask aid and dynamic pseudo-label support. The studies are conducted on three separate road datasets, which consist primarily of high-resolution remote-sensing satellite photos and drone images. The experimental findings suggest that our methodology performs better than advanced scribble-supervised approaches and specific traditional fully supervised methods.