SRPAR: anchor-free detector with aspect ratio priority for slender objects

Slender objects are more difficult to detect than conventional objects with regular shapes, and the rotation of these objects makes detecting them even more challenging. To address these challenges, we proposed SRPAR, an anchor-free detector with aspect ratio priority for slender objects. The aspect ratio priority factor is designed based on the object's aspect ratio and rotation angle. The aspect ratio priority factor guides the regression of slender rotating objects and improves the regression accuracy. In addition, through the multi-level prediction of feature pyramid network, the range of bounding boxes and corresponding angular regressions at each level is limited, and the regression of overlapping object prediction boxes is accelerated. To better evaluate the SRPAR's detection performance of slender rotating objects having an aspect ratio of at least 3:1, some images of sticks are supplemented into the baseball bat subset of the common objects in context (COCO) dataset to form a new self-made COCO-Stick dataset. Experimental results on the dataset of object detection in aerial images dataset and the self-made COCO-Stick dataset show that, compared with state-of-the-art detectors, the proposed method has some advantages in detection accuracy.