Fine-grained object detection, capable of identifying subcategories or types, is thriving in remote sensing scenes. In practice, most existing fine-grained detectors are derived from the two-stage RCNN paradigm with intricate anchor boxes, focusing on refining features of region-of-interests to boost performance, often incurs a redundant process. By contrast, the one-stage, anchor-free paradigm possesses a simple yet effective pipeline, but its exploration in fine-grained detections is still far from sufficient. In this paper, we propose a one-stage, anchor-free fine-grained detector for remote sensing aircraft recognition. We initially delve into predominant issues when extending the one-stage framework to conduct fine-grained detections, typified by severe inter-class confusion and inferior performance in rare categories. Then, we design a fine-grained classification branch, including a Region-to-Region Context Distributor (R2CD), a Class-aware Decoupled Focal Loss (CDFL), and a Cross-Shaped Sample Space (CS3), to address these hindrances. Specifically, the R2CD flexibly integrates the sparse attention mechanism with mask prediction operations to conduct region-level content interactions separately within the foreground and background of feature maps, significantly alleviating the inter-class confusion by enhancing subtle features; the CDFL employs dynamic modulation factors driven by optimization gradients to regulate loss contributions across categories while optimizing category-specific heatmaps, thus prioritizing rare categories with hard samples; the CS3 attains a preferable assignment strategy of positive and negative samples by incorporating structure prior, facilitating the capture of foreground features. Extensive experiments conducted on the MAR20 and FAIRPlane11 datasets demonstrate that our model excels at distinguishing fine-grained categories and is well-suited for performing fine-grained detection tasks.

Computer vision and image processing techniques have been widely applied to power transmission line inspection. However, the successful detection of small targets in large scenes is still challenging due to their low resolution and poor feature representation. Existing methods, such as multi-scale image pyramid, multi-scale feature pyramid and multiple heterogeneous feature fusion, etc. can extract more representative features of small objects, but they usually require high computation cost. In this paper, we propose an effective two cascaded Faster R-CNN strategy, which is based on multi-scale features and semantic information between the objects and the background, to address the small target detection in large scenes. Specially, we detect large object candidate proposals that may contain small objects at first and then map them to the original images to detect the small-sized targets on the high resolution regions. Experiments show that our strategy could lead to higher (83.0%) accuracy of small target detection and recognition than the one-stage Faster R-CNN (78.3%) on the dataset of aerial images.