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

Area of Science:

• Computer Vision
• Machine Learning
• Object Detection

Background:

• Arbitrary-oriented object detection (AOOD) is crucial in various fields.
• Existing AOOD methods often rely on anchor-boxes or standard Gaussian heatmaps, limiting their ability to capture object orientation and shape characteristics.
• These traditional methods can also require extensive parameter tuning.

Purpose of the Study:

• To propose a novel AOOD method, General Gaussian Heatmap Label Assignment (GGHL), that overcomes limitations of existing label assignment strategies.
• To enhance the accuracy and efficiency of detecting arbitrarily oriented objects.
• To provide a generally applicable method that can improve performance across various AOOD techniques.

Main Methods:

• Developed an anchor-free object-adaptation label assignment (OLA) strategy using 2D oriented Gaussian heatmaps to define positive candidates.
• Introduced an oriented-bounding-box (OBB) representation component (ORC) for adaptive Gaussian center prior weight adjustment.
• Designed a joint-optimization loss (JOL) incorporating area normalization and dynamic confidence weighting for refining subtask results.

Main Results:

• The proposed GGHL method significantly improves arbitrary-oriented object detection performance.
• GGHL demonstrates effectiveness with reduced parameter-tuning and time costs.
• Experiments show GGHL enhances performance across various AOOD methods, including lightweight models for embedded systems.

Conclusions:

• GGHL offers an effective and efficient solution for arbitrary-oriented object detection.
• The method's adaptability makes it a valuable enhancement for existing AOOD frameworks.
• GGHL shows promise for real-world applications, especially on resource-constrained embedded platforms.