Tiny object detection (TOD) in remote sensing imagery remains challenging because foreground signals are extremely weak in deep feature hierarchies and are easily overwhelmed by high-response background interference. To mitigate this observed foreground-background signal modulation imbalance (FBSMI) difficulty, we propose a signal modulation network (SMN) for remote-sensing TOD. SMN comprises two complementary components. First, an adaptive Wiener filter modulator (AWFM) is inserted after backbone stages to suppress background-dominated noise while preserving weak target-related responses at multiple resolutions. Second, we introduce the novel denoising diffusion transformer (DDT), a feature-space conditional diffusion module that operates on detector feature tensors rather than image pixels. DDT generates multiple diffusion-guided semantic feature variants from high-level fused features and expands the local representation space around weak tiny object evidence. Extensive experiments on AI-TOD, SODA-A, DOTAv2.0, and DIOR-R demonstrate that SMN not only effectively mitigates the FBSMI problem, but also improves detection accuracy, particularly for very tiny and tiny objects, compared with state-of-the-art methods.

Efficient tiny object detection (TOD) in large-size remote sensing imagery (LSRSI) is particularly challenging in real-world remote sensing applications. We observe that as the input size of the remote sensing scene increases, TOD faces more severe foreground signal identification issues. To address this, we are the first to design a backbone network from the perspective of low-level spatial feature preservation and utilization, specifically for tiny object feature extraction in large-size remote sensing scene patches. The proposed architecture, referred to as the resolution preserving and utilization network (RPUN), demonstrates excellent foreground tiny object feature response identification ability when increasing the input size of remote sensing scenes, effectively maintaining detection performance comparable to that of smaller input slices. Additionally, we introduce GF2UBSv2, a large-scale panchromatic satellite imagery dataset focused on tiny urban bridge detection. Extensive experiments conducted on GF2UBSv2, DIOR, SODA-A, and DOTAv2.0 demonstrate the superior performance of RPUN compared with state-of-the-art methods. The code and dataset are available at: https://github.com//Nankle.

Object Detection is a fundamental procedure in the interpretation of remote sensing images. In large-scale remote sensing images, it is common to observe that the interesting objects only occupy a small area. Such objects provide limited information gain and exhibit unclear edges, often named as small objects. The inherent characteristics of small objects significantly hinder the precise localization and accurate classification of deep object detection networks. In this paper, we introduce a significant challenge: the presence of similar objects among these small objects, which leads to dramatic misclassification and overall accuracy decrease. To assess this phenomenon, we propose a novel metric, Similar Category Angle (SCA), for classification discrimination, which serves to intuitively describe the network’s effectiveness in discriminating similar category objects in its final predictions. We also propose a one-stage object detection network named Similar Category Enhancement Network (SCENet), designed to tackle the challenges associated with discriminating similar objects in small object detection tasks. Specifically, we design SCA Loss guided by the SCA metric, which integrates SCA into the network training process, thereby enhances the network’s capability to discriminate between similar category objects. Meanwhile, we propose Laplacian Sobel Enhancement FPN, LSE-FPN, a module that incorporates dynamic edge extraction operators into the FPN to enhance the network’s ability to detect small objects by sharpening the explicit edges of objects in the feature map. Extensive experiments conducted on SODA-A, VisDrone2019 and FAIR1M-AIR datasets demonstrate the superiority of SCENet in the small object detection task, with significant improvements in detection results for both the mAP50 and SCA metrics. The code is available at https://github.com/weiziji01/SCENet.

The detection of objects with larger aspect ratios (OLAR) is a challenging problem in a special application scenario, such as remote sensing object recognition and scene text detection. However, current object detectors perform poorly in OLAR feature extraction because they are incapable of adaptively responding to object shapes, which leads to severe misalignment between impure feature representations and region proposals. In this letter, we aim at solving this problem by proposing our shape-sensitive convolution network (SSC-Net). SSC-Net is carefully embedded with a feature enhancement module (SSC module) specifically suitable for OLAR. This module can use fewer sampling points to achieve more intelligent feature sampling area transformation, thus achieving the goal of enhancing OLAR feature representation. Extensive experiments on benchmark datasets that are rich in OLARs have proved the superiority of our method. Besides, we further verified the plug-and-play performance of the SSC module, and the experimental results show that it can significantly improve the detection performance of the detector for OLAR.

Arbitrary-oriented object detection (AOOD) is widely used in aerial images because of its efficient object representation. However, current detectors employ the over-standardized feature extraction structure, resulting in detectors has no ability to adaptively readjust feature representations of detection units. Meanwhile, we observe that many detection units could not focus on the objects of interest in their receptive field and are easily affected by the background information and interference targets, leading to the weaking of feature expression ability. We call them sub-optimal detection units. To address this issue, we propose a novel feature enhancement module called fountain feature enhancement module (FFEM). FFEM ingeniously uses the fountain-like structure to reconstruct the features of sub-optimal detection units, generating fountain features that can automatically condense spatial regional features, which effectively enhances detectors’ overall representation ability. Then, a high-performance AOOD detector called fountain fusion net (FFN) is proposed with FFEM embedded, and many novel AOOD components are tested for their progressiveness. We validated our FFN and FFEM using three remote sensing datasets ‒ DOTA, HRSC2016, and UCAS-AOD as well as one scene text dataset‒ICDAR 2015. Extensive experiments demonstrate the effectiveness of our proposed method on improving current detectors to achieve state-of-the-art performance based on this novel idea.