Achieving Continuous Representation of Oriented Bounding Boxes in Object Detection

Keskeiset käsitteet

This paper introduces Continuous OBB (COBB) to address discontinuity issues in Oriented Bounding Box representation, ensuring both encoding and decoding continuity.

Tiivistelmä

Efforts to achieve continuous representation of Oriented Bounding Boxes (OBBs) are crucial for accurate object detection. The proposed COBB method guarantees continuity in bounding box regression, outperforming existing methods on popular datasets like DOTA.
Considerable research has been dedicated to resolving the discontinuity challenges in OBB representations for Object Detection. Existing methods struggle with rotation and aspect ratio discontinuities, leading to precision errors and hindering prediction accuracy. The introduction of COBB aims to provide a fully continuous representation of OBBs, addressing both encoding and decoding discontinuities effectively.
Theoretical analysis supports the effectiveness of COBB in ensuring continuity under defined metrics. By introducing formal metrics for assessing continuity, this method offers a comprehensive solution to the inherent challenges in existing OOB representations. Benchmarking results demonstrate the superiority of COBB over peer methods, showcasing significant improvements in mAP values across various datasets and detectors.

Tilastot

Our new method outperforms Gliding Vertex by 1.13% mAP50 and 2.46% mAP75 on the DOTA dataset.
Achieves a 3.95% improvement in mAP75 when applied to Faster R-CNN on the DOTA Dataset.

Lainaukset

"Existing methods aim at resolving encoding discontinuity but often introduce decoding discontinuity, leading to precision errors."
"Our COBB method ensures both encoding and decoding continuity for accurate object detection."

Tärkeimmät oivallukset

Theoretically Achieving Continuous Representation of Oriented Bounding Boxes

by Zikai Xiao,G... klo arxiv.org 03-01-2024

https://arxiv.org/pdf/2402.18975.pdf

Syvällisempiä Kysymyksiä

How can the concept of continuous representation be applied beyond object detection

Continuous representation can be applied beyond object detection in various fields where precise and continuous representations are crucial. For example, in robotics, continuous representations of objects can enhance robot manipulation tasks by providing accurate information about the shape and orientation of objects. This can lead to improved grasping strategies and object manipulation capabilities. In autonomous vehicles, continuous representations of obstacles and road structures can improve navigation and decision-making processes. Additionally, in medical imaging, continuous representations of anatomical structures can aid in more accurate diagnoses and treatment planning.

What counterarguments exist against achieving complete continuity in OOB representations

Counterarguments against achieving complete continuity in OOB representations may include:

Complexity: Achieving complete continuity may require complex mathematical models or algorithms that could increase computational complexity.
Trade-offs: There might be trade-offs between achieving perfect continuity and other factors such as model efficiency or interpretability.
Data Variability: Real-world data may have inherent variability that makes it challenging to achieve complete continuity across all scenarios.
Practical Constraints: Implementing completely continuous representations may not always be feasible due to practical constraints such as limited resources or time constraints.

How does the theoretical guarantee of continuity impact practical applications beyond benchmarking

The theoretical guarantee of continuity has significant implications for practical applications beyond benchmarking:

Improved Model Performance: Models utilizing theoretically guaranteed continuous OOB representations are likely to perform better in real-world scenarios with varying object orientations.
Enhanced Robustness: Continuity ensures that predictions remain stable under small perturbations, leading to more robust models capable of handling uncertainties.
Better Generalization Theoretical guarantees of continuity can help models generalize better to unseen data by ensuring consistent predictions across different instances.
Real-World Applications Practical applications like robotics, autonomous systems, healthcare technologies benefit from the reliability provided by theoretically guaranteed continuities for making critical decisions based on accurate object representation.