Huang, ZhanchaoLi, WeiXia, Xiang-GenTao, Ran2022-03-282022-03-282022-02-09Z. Huang, W. Li, X. -G. Xia and R. Tao, "A General Gaussian Heatmap Label Assignment for Arbitrary-Oriented Object Detection," in IEEE Transactions on Image Processing, vol. 31, pp. 1895-1910, 2022, doi: 10.1109/TIP.2022.3148874.1941-0042https://udspace.udel.edu/handle/19716/30715Copyright 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This article was originally published in IEEE Transactions on Image Processing. The version of record is available at: https://doi.org/10.1109/TIP.2022.3148874Recently, many arbitrary-oriented object detection (AOOD) methods have been proposed and attracted widespread attention in many fields. However, most of them are based on anchor-boxes or standard Gaussian heatmaps. Such label assignment strategy may not only fail to reflect the shape and direction characteristics of arbitrary-oriented objects, but also have high parameter-tuning efforts. In this paper, a novel AOOD method called General Gaussian Heatmap Label Assignment (GGHL) is proposed. Specifically, an anchor-free object-adaptation label assignment (OLA) strategy is presented to define the positive candidates based on two-dimensional (2D) oriented Gaussian heatmaps, which reflect the shape and direction features of arbitrary-oriented objects. Based on OLA, an oriented-bounding-box (OBB) representation component (ORC) is developed to indicate OBBs and adjust the Gaussian center prior weights to fit the characteristics of different objects adaptively through neural network learning. Moreover, a joint-optimization loss (JOL) with area normalization and dynamic confidence weighting is designed to refine the misalign optimal results of different subtasks. Extensive experiments on public datasets demonstrate that the proposed GGHL improves the AOOD performance with low parameter-tuning and time costs. Furthermore, it is generally applicable to most AOOD methods to improve their performance including lightweight models on embedded platforms.en-USArbitrary-oriented objectconvolutional neural networkgaussian heatmapobject detectionlabel assignmentArticle