Oriented Object Detection with Transformer
Object detection with Transformers (DETR) has achieved a competitive performance over traditional detectors, such as Faster R CNN. However, the potential of DETR remains largely unexplored for the more challenging task of arbitraryoriented object detection problem. We provide the first attempt and implement Oriented Object DEtection with TRansformer (ODETR) based on an end to end network. The contributions of ODETR include: 1) we provide a new insight into oriented object detection, by applying Transformer to directly and efficiently localize objects without a tedious process of rotated anchors as in conventional detectors; 2) we design a simple but highly efficient encoder for Transformer by replacing the attention mechanism with depthwise separable convolution, which can significantly reduce the memory and computational cost of using multi scale features in the original Transformer; 3) our ODETR can be another new benchmark in the field of oriented object detection, which achieves up to 3.85 mAP improvement over Faster R CNN and RetinaNet. We simply fine tune the head mounted on ODETR in a cascaded architecture and achieve a competitive performance over SOTA in the DOTA dataset.
ArXiv