Horizontal Box Supervised Oriented Object Detection Based on Edge Feature Self-Attention and Multitask Synthetic Visual Patterns

With the advancement of object detection, oriented object detection is gradually replacing horizontal object detection as the preferred solution in remote sensing images. However, most existing datasets are annotated with horizontal bounding boxes, while re-annotating these datasets is both costly and inefficient. Recently, horizontal box supervised oriented object detection methods using merely horizontal box annotations have emerged as a solution. Nonetheless, these methods are relatively poor in predicting angles because of lacking angle supervision. In addition, these methods overlook edge features, which can help capture and represent the oriented boundaries between objects and backgrounds, especially for objects with distinct edges or in blank backgrounds. To address these issues, we propose an edge feature self-attention and multitask synthetic pattern enhanced horizontal box supervised oriented object detector named EAMSDet, aiming to achieve high precision based on horizontal box annotations. Specifically, we use horizontal and vertical Sobel operators to extract edge information and a self-attention module to fuse the edge information with the feature maps. Furthermore, to enrich the supervision of aspect ratio and angles, we add a multitask synthetic branch and an extra regression loss to make full use of our introduced synthetic visual patterns. Based on H2RBox-v2, we conducted extensive experiments on remote sensing datasets such as DOTA-v1.0, DIOR, and HRSC2016, demonstrating the effectiveness and generalizability of our EAMSDet.