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Rotated Object Detection of Remote Sensing Image Based on Binary Smooth Encoding and Ellipse-Like Focus Loss

Remote sensing image object detection has been widely developed in many applications. Objects in remote sensing data have the characteristic of arbitrary directions, which leads to poor detection performance based on horizontal box detectors. To address this issue, a novel rotated object detection model based on binary smooth encoding and ellipse-like focus loss is proposed in this letter. First, a multilayer feature fusion network with an attention mechanism is developed to extract features of multiscale objects. Then, an anchor-free detection module with binary smooth encoding is proposed, which aims to predict the rotated angles of objects. Moreover, an ellipse-like focus loss is proposed to obtain high-quality bounding boxes drawing near the object center. Experimental results on two public remote sensing datasets verify that the proposed method can yield superior detection performance than other related rotated object detection models.