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.

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.

Arbitrary‐oriented object detection is widely used in aerial image applications because of its efficient object representation. However, the use of oriented bounding box aggravates the imbalance between positive and negative samples when using one‐stage object detectors, which seriously decreases the detection accuracy. We believe that it is the anchor learning strategy (ALS) used by such detectors that needs to take the responsibility. In this study, three perspectives on ALS design were summarised and ALS—Performance Releaser with Smart Anchor Learning (PRSAL) was proposed. Performance Releaser with Smart Anchor Learning is a dynamic ALS that utilises anchor classification ability as an equivalent indicator to anchor box regression ability, this allows anchors with high detection potential to be filtered out in a more reasonable way. At the same time, PRSAL focuses more on anchor potential and it is able to automatically select a number of positive samples that far exceed that of other methods by activating anchors that previously had a low spatial overlap, thereby releasing the detection performance. We validate the PRSAL using three remote sensing datasets—HRSC2016, DOTA and UCAS‐AOD as well as one scene text dataset—ICDAR 2013. The experimental results show that the proposed method gives substantially better results than existing models.

Arbitrary-oriented object detection (AOOD) is a crucial task in aerial image analysis but is also faced with significant challenges. In current AOOD detectors, commonly used multi-scale feature fusion modules fall short in spatial and semantic information complement between scales. Additionally, fixed feature extraction structures are usually used following a fusion model, resulting in the inability of detectors to self-adjust. At the same time, feature fusion and extraction modules are designed in isolation and the internal synergy between them is ignored. The above problems result in feature representation deficiency, thus affecting the overall detection precision. To solve these problems, we first create a fine-grained feature pyramid network (FG-FPN) that not only provides richer spatial and semantic features, but also completes neighbor scale features in a self-learning mode. Subsequently, we propose a novel feature enhancement module (FEM) to fit FG-FPN. FEM authorizes the detection unit to automatically adjust the sensing area and adaptively suppress background interference, thereby generating stronger feature representations. Our proposed solution was tested through extensive experiments on challenging datasets, including DOTA (77.44% mAP), HRSC2016 (97.82% mAP), UCAS-AOD (91.34% mAP), as well as ICDAR2015 (86.27% F-score) and its effectiveness and high applicability are verified on all the above datasets.

Ship detection in optical remote sensing imagery is pivotal for various civilian and military applications. However, due to the presence of cluttered backgrounds and the significant variations in ship scales, accurately detecting ships in complex maritime environments remains challenging. To address these challenges, we propose the Dual Attention and Scale-aware Feature Alignment Network (DASFA-Net), to effectively detect multi-scale ship targets under clutter interference. The DASFA-Net comprises two primary components: Deformable Spatial Attention Module with Channel Integration (DSAM-CI) and Bidirectional Flow Alignment Network (BiFAN). The DSAM-CI enhances discrimination between ships and distractors by jointly modeling correlations across the channel and spatial domains and applying deformable spatial attention. Concurrently, BiFAN employs a bidirectional feature flow alignment strategy to resolve spatial misalignment in feature fusion, significantly improving the quality of multi-scale feature fusion. Lastly, we design the Dynamic Alpha Complete Intersection over Union (DA-CIoU) based on prior knowledge of target characteristics to explicitly refine our detector for maritime ships. Extensive experiments on three challenging ship detection benchmarks, including HRSC2016, SCCOS, and FGSRCS have demonstrated the superiority of our DASFA.

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.