Oriented object detection in remote sensing images based on angle quality estimation

Remote sensing has a large amount of content information and high complexity, making it difficult to annotate image, high demand for professional knowledge, and high economic cost. There are significant differences in the quality of data labels obtained, which poses various information limited challenges in object detection. Data labels with significant differences in quality pose various challenges with limited information for object detection. Therefore, a new oriented object detection technique based on angle quality estimation (AQE-detector) was designed to overcome the challenges of missing confidence information on oriented angle and insufficient object localization accuracy. Firstly, a periodic Gaussian distribution was used to model the oriented angle variable, transforming the angle regression into the distribution estimation, greatly improving the accuracy of the angle and implicitly estimating the potential confidence. Based on this, the non-maximum suppression based on angle quality (NMS-AQ) was proposed to alleviate the confirmation bias caused by existing methods that only use classification confidence to evaluate detection results. An angle loss function based on aspect-ratio perception (Double-A Loss) was designed to effectively improving the overall detection performance. The intelligent object detection technology under information limited conditions has been studied, facilitating the efficient application of object detection in major demand fields, such as remote sensing and non-destructive testing.