Adaptive Rotated Convolution for Rotated Object Detection (2303.07820v2)

Abstract: Rotated object detection aims to identify and locate objects in images with arbitrary orientation. In this scenario, the oriented directions of objects vary considerably across different images, while multiple orientations of objects exist within an image. This intrinsic characteristic makes it challenging for standard backbone networks to extract high-quality features of these arbitrarily orientated objects. In this paper, we present Adaptive Rotated Convolution (ARC) module to handle the aforementioned challenges. In our ARC module, the convolution kernels rotate adaptively to extract object features with varying orientations in different images, and an efficient conditional computation mechanism is introduced to accommodate the large orientation variations of objects within an image. The two designs work seamlessly in rotated object detection problem. Moreover, ARC can conveniently serve as a plug-and-play module in various vision backbones to boost their representation ability to detect oriented objects accurately. Experiments on commonly used benchmarks (DOTA and HRSC2016) demonstrate that equipped with our proposed ARC module in the backbone network, the performance of multiple popular oriented object detectors is significantly improved (\eg +3.03\% mAP on Rotated RetinaNet and +4.16\% on CFA). Combined with the highly competitive method Oriented R-CNN, the proposed approach achieves state-of-the-art performance on the DOTA dataset with 81.77\% mAP. Code is available at \url{https://github.com/LeapLabTHU/ARC}.

• The paper's primary contribution is the ARC module that adaptively rotates convolution kernels based on the input data's orientation.
• It integrates adaptive kernel rotation with conditional computation, yielding improvements of +3.03% to +4.16% mAP on standard benchmarks.
• The method enhances various vision backbones and lays the groundwork for dynamic, adaptable feature extraction in computer vision tasks.

Analyzing Adaptive Rotated Convolution for Rotated Object Detection

The paper "Adaptive Rotated Convolution for Rotated Object Detection" introduces an innovative approach to enhance rotated object detection by utilizing an Adaptive Rotated Convolution (ARC) module. The ARC module is designed to overcome the challenges present when detecting objects with arbitrary orientations in images, which is a common problem in fields such as aerial image recognition, scene text detection, and face detection. This paper addresses a notable gap in the design of backbone networks that struggle to effectively capture features of non-aligned objects, which frequently vary in orientation across and within images.

Key Contributions

The primary contribution of the paper is the introduction of the ARC module. This module fundamentally enhances the feature extraction process by enabling convolutional kernels to rotate adaptively in response to the orientation of objects within images. Two critical innovations are incorporated within the ARC module:

• Adaptive Kernel Rotation: This mechanism enables the convolution kernels to rotate according to the orientation of the input data, adapting dynamically rather than being constrained by static orientations. This dynamic adaptation stems from a data-dependent routing function that predicts rotation angles for the kernels.
• Conditional Computation Mechanism: This approach enhances the detector's efficiency and adaptability by allowing multiple kernels to rotate and compute individually, ultimately combining them. It improves the network's representational power by effectively addressing the issue of varying object orientations within an image.

Incorporation of these techniques allows ARC to seamlessly integrate into various vision backbones, boosting their capacity to detect oriented objects.

Experimentation

The paper presents comprehensive experimental evaluations conducted on well-regarded benchmarks such as DOTA and HRSC2016. These experiments reveal that integrating the ARC module into several popular oriented object detectors significantly elevates their performance. Notable improvements were observed, including a +3.03% mAP increase on Rotated RetinaNet and a +4.16% jump for CFA, showcasing the wide applicability and impact of the ARC module. Moreover, when fused with Oriented R-CNN, the enhanced approach achieved state-of-the-art performance on the DOTA dataset, achieving an impressive 81.77% mAP, which underscores its efficacy.

Implications and Future Directions

The paper's contribution extends beyond achieving high empirical results; it sets the foundation for future research on adaptable feature extraction mechanisms tailored for specialized detection tasks. The widening of the parameter space by ARC’s techniques can inspire further exploration into dynamic network architectures that balance flexibility and computational cost for diverse computer vision tasks.

Potential future research could delve into refining the routing function for even more precise angle prediction and exploring more complex strategies for kernel combination, perhaps employing learnable schemes. Additionally, examining how ARC-like modules could benefit other tasks involving geometric transformations, such as 3D object detection or unsupervised representation learning, might offer intriguing possibilities.

In conclusion, the authors present a methodologically solid and empirically validated advancement in rotated object detection, presenting an adaptive solution that holds promise for enhancing both practical applications and theoretical understanding of dynamic neural networks.