Single-stage oriented object detection via Corona Heatmap and Multi-stage Angle Prediction

Abstract

Oriented-object detection is a pivotal research subject within computer vision. Nevertheless, extant-oriented detection methodologies frequently grapple with boundary problems when handling targets with large aspect ratios and arbitrary orientations. This study introduces a Single-stage oriented-object detection via Corona Heatmap and Multi-stage Angle Prediction (SOD) to address the above challenges. Specifically, the angle is conceptualised as a multi-stage classification prediction; the initial stage forecasts the approximate rotational direction of the target. Based on the outcome of the first stage, the subsequent stage uses Gaussian smoothing encoding to predict the precise angle, mitigating discrepancies caused by floating-point representation. Moreover, this study proposes a spatially constrained corona heatmap to rectify the issue of central position prediction drift in the Gaussian heatmap when dealing with targets with large aspect ratios. The post-processing methodology is refined based on the above approach, effectively mitigating the deviation of the predicted target’s central position of the predicted target. Visual results and experimental analyses conducted on multiple challenging datasets corroborate the efficacy and competitiveness of the proposed method.