Recently, 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 (2-D) 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.
翻译:最近,在许多领域提出了许多任意导向物体探测(AOOD)方法,并引起广泛关注,然而,大多数这些方法都以锚框或标准高斯制热映射仪为基础。这种标签分配战略可能不仅不能反映任意定向对象的形状和方向特征,而且还有很高的参数调控努力。在本文件中,提出了名为Gaussian Heatmap Label Acel AOOD(GGHL)的新型方法。具体地说,提出了一种无锚定型的物体调整定位标签分配(OLA)战略,以基于双维方向的高斯制热映射仪(2-D)来界定积极候选人,反映任意定向对象的形状和方向特征特征,这种标签分配战略可能不仅不能反映任意定向对象对象的物体的形状和方向特征,而且还具有很高的参数。根据OLA(OB)开发了一个定向约束框(OBB)表示OBBS和调整高斯中心先前的重量,以适应不同物体的特性,通过神经网络平台学习适应。此外,还提出了联合优化损失(JOL)与区域正常化和最动态的GODOD模型,包括最优化的微调度的性性能模型。