Object detection is a fundamental yet challenging task in computer vision. Despite the great strides made over recent years, modern detectors may still produce unsatisfactory performance due to certain factors, such as non-universal object features and single regression manner. In this paper, we draw on the idea of mutual-assistance (MA) learning and accordingly propose a robust one-stage detector, referred as MADet, to address these weaknesses. First, the spirit of MA is manifested in the head design of the detector. Decoupled classification and regression features are reintegrated to provide shared offsets, avoiding inconsistency between feature-prediction pairs induced by zero or erroneous offsets. Second, the spirit of MA is captured in the optimization paradigm of the detector. Both anchor-based and anchor-free regression fashions are utilized jointly to boost the capability to retrieve objects with various characteristics, especially for large aspect ratios, occlusion from similar-sized objects, etc. Furthermore, we meticulously devise a quality assessment mechanism to facilitate adaptive sample selection and loss term reweighting. Extensive experiments on standard benchmarks verify the effectiveness of our approach. On MS-COCO, MADet achieves 42.5% AP with vanilla ResNet50 backbone, dramatically surpassing multiple strong baselines and setting a new state of the art.

Current two-stage oriented object detectors for aerial images have achieved remarkable progress. However, they still suffer from some drawbacks. Firstly, most of them place redundant anchors or utilize complicated transformation to generate oriented proposals, which are inefficient. Secondly, the generation of proposals is static, which cannot adapt to the extremely nonuniform distribution of objects. To address these issues, we propose a Dynamic Proposal Generation Network (DPGN) which can generate high-quality oriented proposals directly and estimate the upper limit of proposals adaptively. To be specific, with Guided Anchor Regression (GAR), we obtain the coarse oriented anchors and utilize them to align the features. After this, we make further classification and regression to produce final oriented proposals. Meanwhile, we design Maximum Number Estimation (MNE) for predicting an approximate value to remain the proposals adaptively. Without tricks, our method can achieve competitive detection accuracy compared with other mainstream methods on DOTA dataset.

Oriented object detection is a key task in the field of remote sensing image interpretation. Although extensive efforts have been made over the past few years, accurate oriented object detection remains a big challenge due to the dense arrangement and diverse orientations of objects. In this paper, we propose an oriented object detector based on the Faster R-CNN, which mainly consists of a Precise Vertex Regression (PVR) module and a Feature Decoupling (FD) module. Specifically, the PVR module predicts the arbitrary quadrilaterals of oriented objects with the precise vertex regression manner, which discretizes the regression range of vertex into several bins and applies a classification network to predict which bin the vertex belongs to. The FD module decouples the RoI features for classification and regression tasks by lightweight affine transformation. Experimental results on DOTA and DIOR-R datasets validate the effectiveness of our proposed method. Code is available at https://github.com/ShichengMiao16/VRDet.

This paper aims to address the problem of oriented object detection under the complex background of remote sensing images. To this end, we propose a one-stage object detection method with feature fusion structure, and modify the loss function to enhance the detection of small objects. More specifically, on the basis of the end-to-end one-stage object detection model RetinaNet, the method of gliding the vertices of the horizontal bounding box is used to describe an oriented object. In order to obtain multi-scale context information, we design a feature fusion module. Besides, we propose a novel area-weighted loss function to pay more attention to small objects. Experimental results conducted on the DOTA dataset demonstrate that the proposed framework outperforms several state-of-the-art baselines.