Spatial Shape-Aware Network for Elongated Target Detection

In remote sensing detection, targets often exhibit unique characteristics such as elongated shapes, multi-directional rotations, and significant scale variations. Traditional convolutional networks extract features using convolution kernels and rely on predefined anchor boxes and sample selection to frame the targets. However, this approach leads to several issues, including imprecise regional feature extraction, the neglect of object shape information, and variations in the potential of positive samples, all stemming from shape variations, ultimately impacting the detector’s performance. To overcome these challenges, we propose a novel Spatial Shape-Aware Network for Elongated Target Detection. Specifically, we introduce three key modules: a Boundary-Guided Spatial Feature Perception Module (BGSF), a Shape-Sensing Module (SSM), and a Potential Evaluation Module (PEM). The Boundary-Guided Spatial Feature Perception Module adjusts the sampling positions and weights of convolution kernels, aligning the feature maps produced by the backbone network to the actual shape and location of the target, while reducing feature responses to irrelevant noise. The Shape-Sensing Module incorporates shape information into the sample selection process, allowing high-potential anchor boxes—which may have low IoU but capture critical target features—to be temporarily retained for further training. The Potential Evaluation Module integrates the potential information of positive samples into the loss function, providing stronger training feedback for high-potential positive samples. Experiments demonstrate that, compared with existing detection networks, our proposed network structure achieves superior detection performance on two widely used datasets, UCAS-AOD and HRSC2016.