Oriented RepPoints for Aerial Object Detection (2105.11111v4)

Abstract: In contrast to the generic object, aerial targets are often non-axis aligned with arbitrary orientations having the cluttered surroundings. Unlike the mainstreamed approaches regressing the bounding box orientations, this paper proposes an effective adaptive points learning approach to aerial object detection by taking advantage of the adaptive points representation, which is able to capture the geometric information of the arbitrary-oriented instances. To this end, three oriented conversion functions are presented to facilitate the classification and localization with accurate orientation. Moreover, we propose an effective quality assessment and sample assignment scheme for adaptive points learning toward choosing the representative oriented reppoints samples during training, which is able to capture the non-axis aligned features from adjacent objects or background noises. A spatial constraint is introduced to penalize the outlier points for roust adaptive learning. Experimental results on four challenging aerial datasets including DOTA, HRSC2016, UCAS-AOD and DIOR-R, demonstrate the efficacy of our proposed approach. The source code is availabel at: https://github.com/LiWentomng/OrientedRepPoints.

• The paper proposes Oriented RepPoints, a novel adaptive point representation that significantly enhances detection precision for arbitrarily-oriented aerial objects.
• It introduces three oriented conversion functions with a quality assessment and sample assignment mechanism to optimize classification and localization.
• Evaluations on datasets like DOTA and HRSC2016 demonstrate improved mAP and reduced mean Average Orientation Error, confirming its robust performance.

Overview of "Oriented RepPoints for Aerial Object Detection"

The paper "Oriented RepPoints for Aerial Object Detection" presents an innovative approach to aerial object detection leveraging adaptive points for more accurate object localization. Unlike traditional methods that rely on bounding box orientations, this work introduces a point set-based representation, termed Oriented RepPoints, which adeptly captures the geometric properties of objects irrespective of their orientation. This technique specifically targets the challenges posed by aerial object detection, including non-axis aligned objects and cluttered environmental contexts.

The central thrust of this research is the method's ability to accurately capture geometric information from arbitrary-oriented instances through adaptive point sets. The authors delineate three oriented conversion functions that enhance classification and precise localization. A significant contribution is the adaptive points learning scheme, which incorporates a novel quality assessment and sample assignment mechanism, selecting high-quality oriented reppoints to improve training outcomes. This involves a spatial constraint added to penalize outlier points, fostering robust adaptive learning.

Numerical Results and Claims

The Oriented RepPoints framework was evaluated on four comprehensive aerial datasets: DOTA, HRSC2016, UCAS-AOD, and DIOR-R. The method demonstrated strong performance metrics, outperforming several state-of-the-art methods. For example, on the DOTA dataset, the method achieved a notable mAP improvement over traditional approaches, underscoring its efficacy in challenging aerial object detection scenarios. The proposed framework not only excelled in terms of precision but also reduced mean Average Orientation Error (mAOE), confirming its superior orientation accuracy.

Implications and Future Directions

From a practical standpoint, the Oriented RepPoints method transforms the landscape of aerial object detection by enhancing precision in environments that contain non-standard object alignments and dense distributions. Theoretically, the approach opens new avenues in adaptive point representation, especially in scenarios where rotation and arbitrary orientations significantly impact detection accuracy.

Future research could extend this framework into real-time applications, optimize its computational efficiency, and explore adaptation in dynamic or changing aerial environments. Further investigation might also explore integration with other remote sensing technologies to broaden its applicability across different domains, such as disaster management or urban planning.

In conclusion, the Oriented RepPoints framework offers a comprehensive solution to aerial object detection challenges. By effectively utilizing adaptive point representations, it markedly advances accuracy and robustness in detecting arbitrarily-oriented objects in aerial imagery, creating substantial opportunities for future exploration and application in computer vision and related fields.