Title: TricubeNet: 2D Kernel-Based Object Representation for Weakly-Occluded Oriented Object Detection (WACV 2022)
Authors: Beomyoung Kim (NAVER CLOVA), Janghyeon Lee (LG AI Research), Sihaeng Lee (LG AI Research), Doyeon Kim (KAIST), Junmo Kim (KAIST)
Abstract: We present a novel approach for oriented object detection, named TricubeNet, which localizes oriented objects using visual cues (i.e., heatmap) instead of oriented box offsets regression. We represent each object as a 2D Tricube kernel and extract bounding boxes using simple image-processing algorithms. Our approach is able to (1) obtain well-arranged boxes from visual cues, (2) solve the angle discontinuity problem, and (3) can save computational complexity due to our anchor-free modeling. To further boost the performance, we propose some effective techniques for size-invariant loss, reducing false detections, extracting rotation-invariant features, and heatmap refinement. To demonstrate the effectiveness of our TricubeNet, we experiment on various tasks for weakly-occluded oriented object detection: detection in an aerial image, densely packed object image, and text image. The extensive experimental results show that our TricubeNet is quite effective for oriented object detection.
