Strip R-CNN: Large Strip Convolution for Remote Sensing Object Detection (2501.03775v4)

Abstract: While witnessed with rapid development, remote sensing object detection remains challenging for detecting high aspect ratio objects. This paper shows that large strip convolutions are good feature representation learners for remote sensing object detection and can detect objects of various aspect ratios well. Based on large strip convolutions, we build a new network architecture called Strip R-CNN, which is simple, efficient, and powerful. Unlike recent remote sensing object detectors that leverage large-kernel convolutions with square shapes, our Strip R-CNN takes advantage of sequential orthogonal large strip convolutions in our backbone network StripNet to capture spatial information. In addition, we improve the localization capability of remote-sensing object detectors by decoupling the detection heads and equipping the localization branch with strip convolutions in our strip head. Extensive experiments on several benchmarks, for example DOTA, FAIR1M, HRSC2016, and DIOR, show that our Strip R-CNN can greatly improve previous work. In particular, our 30M model achieves 82.75% mAP on DOTA-v1.0, setting a new state-of-the-art record. Our code will be made publicly available.Code is available at https://github.com/YXB-NKU/Strip-R-CNN.

• The paper introduces Strip R-CNN, a novel framework that utilizes sequential orthogonal large strip convolutions to enhance the detection of high aspect ratio objects in remote sensing images.
• Experimental results show that Strip R-CNN achieves state-of-the-art performance on various datasets, significantly improving mean average precision, particularly for slender objects.
• The findings suggest re-evaluating non-square kernels in computer vision and offer a pathway to more efficient object detection for practical remote sensing applications.

Analysis of "Strip R-CNN: Large Strip Convolution for Remote Sensing Object Detection"

The increasing interest in remote sensing object detection necessitates the development of techniques that effectively handle the challenges posed by varied and high aspect ratio objects in aerial imagery. The paper "Strip R-CNN: Large Strip Convolution for Remote Sensing Object Detection" addresses these challenges by introducing a novel approach centered around large strip convolutions, forming the core of their proposed Strip R-CNN framework.

Core Contribution

The main contribution of the paper is the Strip R-CNN framework, which leverages sequential orthogonal large strip convolutions to enhance the detection performance of high aspect ratio objects in remote sensing scenarios. The authors argue that traditional convolutional networks, with their standard square-shaped kernels, fail to efficiently capture the elongated structures prevalent in these high aspect ratio objects. Instead, the proposed large strip convolutions offer a more targeted approach by extracting features within elongated spatial domains, thus reducing feature redundancy and capturing crucial spatial dependencies more effectively.

Network Design

The novel network architecture, Strip R-CNN, integrates these large strip convolutions within both the backbone and the detection head of the network. Specifically, the authors introduce the StripNet backbone and a modified strip head. The StripNet backbone uses strip convolutions as essential components that sequentially process horizontal and vertical feature strips, allowing for improved feature extraction across different spatial dimensions. The detection head is augmented by incorporating a decoupled structure that enhances angle prediction and feature localization capability.

Experimental Validation

Extensive experiments are conducted, showcasing the superiority of Strip R-CNN across several standard datasets like DOTA, HRSC2016, FAIR1M, and DIOR. The authors report significant performance improvements, with Strip R-CNN-S achieving up to 82.75% mAP on DOTA-v1.0, setting a new state-of-the-art record. The success is attributed to the improved handling of difficult-to-detect slender objects, with the proposed strip convolutions efficiently capturing essential features while maintaining reduced computational overhead compared to previous large-kernel-oriented methods.

Theoretical and Practical Implications

The use of large strip convolutions challenges the prevailing paradigm in remote sensing object detection that emphasizes square-shaped kernels. The insights from this research suggest reevaluating the convolution shapes used in various vision tasks, especially in cases involving non-uniform object structures. The practical implications are significant, offering a pathway to more efficient and robust object detection in remote sensing applications, which are essential for fields like geographic information systems, military surveillance, and environmental monitoring.

Future Directions

The research opens several avenues for future exploration. First, the principles behind large strip convolutions can be further adapted and optimized for specific remote sensing tasks or other fields with similar detection challenges. Second, integrating this approach with other modern architectures, like transformers which naturally model long-range dependencies, could yield further advancements. Lastly, a comprehensive analysis of computational efficiency and resource utilization across different hardware platforms could provide deeper insights into the broader applicability of strip convolutions in large-scale systems.

In conclusion, this paper presents a compelling approach to improving remote sensing object detection through the innovative use of large strip convolutions. The results indicate strong potential for the wider adoption of such techniques in handling high aspect ratio variations, ultimately contributing to more efficient and accurate object detection frameworks in complex imaging scenarios.