Dual-Gaussian Distribution Location Loss for Real Oriented Object Detection in Remote Sensing Image

In general object detection tasks, accurate responses to the boundaries and classifications of potential objects are required of detectors. Given the diverse shapes of objects, these bounding boxes are typically represented as horizontal rectangles. However, in aerial imagery scenarios, horizontal bounding boxes can easily miss valid detections when objects are densely distributed. In recent years, oriented object detection algorithms have achieved considerable success. However, they focus on the boundaries of the object without taking into account its head–tail attributes and true orientation. Getting the true orientation of objects serves as the long-term aim of remote sensing object detection task. To resolve these issues, we decompose the single Gaussian distribution probability loss into a sequential dual-probability distribution, thereby establishing a one-to-one collaborative loss mechanism. This enables the model to learn the real orientation relationship from the dual distribution loss, achieving the acquisition of objects’ true orientation information. To more accurately reflect the preference of detectors, we design a new evaluation metric based on the cosine function and average precision. We conduct a series of experiments on the DOTA dataset, HRSC dataset, and FAIR1M dataset, achieving satisfactory results that validate the effectiveness of the proposed method.