MMRotate: A Rotated Object Detection Benchmark using PyTorch (2204.13317v4)

Abstract: We present an open-source toolbox, named MMRotate, which provides a coherent algorithm framework of training, inferring, and evaluation for the popular rotated object detection algorithm based on deep learning. MMRotate implements 18 state-of-the-art algorithms and supports the three most frequently used angle definition methods. To facilitate future research and industrial applications of rotated object detection-related problems, we also provide a large number of trained models and detailed benchmarks to give insights into the performance of rotated object detection. MMRotate is publicly released at https://github.com/open-mmlab/mmrotate.

MMRotate: A Comprehensive Rotated Object Detection Framework

The paper "MMRotate: A Rotated Object Detection Benchmark using PyTorch" introduces the MMRotate toolbox, a sophisticated platform designed for researchers and practitioners focusing on rotated object detection. The toolbox provides an open-source, versatile algorithmic framework that streamlines training, inference, and evaluation processes for rotated object detection applications.

Core Contributions

MMRotate implements 18 state-of-the-art algorithms and efficiently supports the three most pervasive angle definition methods: OpenCV, long edge 90°, and long edge 135°. This flexibility accommodates diverse object detection tasks where oriented bounding boxes (OBBs) are utilized. OBBs are preferred over horizontal bounding boxes in scenarios like aerial image detection and text detection due to their ability to align more accurately with object orientations.

The toolbox enhances code reusability and simplifies algorithm implementation by proposing a unified framework. Moreover, the provision of pre-trained models and extensive benchmarks allow for reliable performance assessment across various algorithms, facilitating both academic research and industrial applications.

Benchmark and Evaluation

Extensive benchmarks were conducted on several datasets using MMRotate, demonstrating its utility and the comparative performance of integrated methods. The flexibility to switch between different angle definitions and backbones, including transformer-based backbones like Swin-T, underscores the toolbox's adaptability. Notably, the KLD loss function achieved superior mean Average Precision (mAP) in experimental evaluations when integrated into certain detection models.

Implications and Future Directions

The introduction of MMRotate significantly advances the landscape of rotated object detection research by offering a comprehensive and flexible framework. This development is pivotal for researchers aiming to conduct fair evaluations and optimize various detection strategies within a widely accessible platform. The paper suggests ongoing enhancements and a call for community participation in its development, indicating future expansions in algorithm support and benchmark optimization.

Conclusion

MMRotate stands as a foundational tool for those engaged in the complex domain of rotated object detection. By consolidating algorithmic diversity and simplifying implementation mechanics, MMRotate facilitates a deeper exploration into the intricacies of object orientation challenges. The continued evolution of MMRotate is anticipated to drive innovations that can be leveraged across both theoretical research and practical deployments in visual detection tasks.