A General Gaussian Heatmap Label Assignment for Arbitrary-Oriented Object Detection

Abstract

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.

16 pages, 13 figures

References in corpus (3)

• Gliding vertex on the horizontal bounding box for multi-oriented object detection
• Dense Attention Fluid Network for Salient Object Detection in Optical Remote Sensing Images
• LO-Det: Lightweight Oriented Object Detection in Remote Sensing Images

Cited by in corpus (8)

• G-Rep: Gaussian Representation for Arbitrary-Oriented Object Detection
• Gaussian Bounding Boxes and Probabilistic Intersection-over-Union for Object Detection
• Guided Hybrid Quantization for Object detection in Multimodal Remote Sensing Imagery via One-to-one Self-teaching
• Oriented object detection in optical remote sensing images using deep learning: a survey
• Task-wise Sampling Convolutions for Arbitrary-Oriented Object Detection in Aerial Images
• Learning to Evaluate Performance of Multi-modal Semantic Localization
• EARL: An Elliptical Distribution aided Adaptive Rotation Label Assignment for Oriented Object Detection in Remote Sensing Images
• Multi-Grained Angle Representation for Remote Sensing Object Detection