Corner Proposal Network for Anchor-free, Two-stage Object Detection (2007.13816v1)

Abstract: The goal of object detection is to determine the class and location of objects in an image. This paper proposes a novel anchor-free, two-stage framework which first extracts a number of object proposals by finding potential corner keypoint combinations and then assigns a class label to each proposal by a standalone classification stage. We demonstrate that these two stages are effective solutions for improving recall and precision, respectively, and they can be integrated into an end-to-end network. Our approach, dubbed Corner Proposal Network (CPN), enjoys the ability to detect objects of various scales and also avoids being confused by a large number of false-positive proposals. On the MS-COCO dataset, CPN achieves an AP of 49.2% which is competitive among state-of-the-art object detection methods. CPN also fits the scenario of computational efficiency, which achieves an AP of 41.6%/39.7% at 26.2/43.3 FPS, surpassing most competitors with the same inference speed. Code is available at https://github.com/Duankaiwen/CPNDet

• The paper introduces a novel two-stage object detection framework that uses anchor-free corner keypoints to generate dynamic object proposals.
• It refines detections using a dual-classification process that reduces false positives and enhances precision compared to traditional methods.
• Evaluation on the MS-COCO dataset shows CPN achieving a mean average precision of 49.2% and competitive FPS, underscoring its practical value.

Overview of the Corner Proposal Network for Anchor-free, Two-stage Object Detection

In recent developments within the field of computer vision, object detection has emerged as a key area due to its wide applicability and inherent challenges. This paper presents a novel two-stage, anchor-free methodology termed the Corner Proposal Network (CPN), which aims to enhance the efficacy of object detection tasks through an efficient and precise framework. Taking advantage of anchor-free techniques for the initial phase of proposal extraction, CPN utilizes corner keypoint combinations to detect potential objects, and subsequently applies a robust classification method to refine these detections.

Methodological Insights

Object detection systems typically grapple with the dual objectives of high recall and precision. This paper carves out a middle path by leveraging the distinct strengths of both two-stage detection systems and anchor-free methodologies. While anchor-based methods, historically predominant in object detection, have focused on fixed-scale bounding boxes, anchor-free techniques such as that implemented in CPN move beyond these confines, utilizing keypoint detection for flexible representation of object geometries.

In the first stage of the proposed CPN framework, it uses anchor-free corner keypoints to generate object proposals. By computing heatmaps to predict the prevalence of keypoints, CPN resolves the task of delineating object boundaries in a highly adaptable manner, capitalizing on the freedom from predetermined anchors to enhance recall, especially in scenarios involving objects with unusual shapes or sizes.

The second stage of CPN introduces a two-step classification process to enhance precision by mitigating false positives commonly encountered in anchor-free methods. The binary classifier weeds out clear non-object proposals, while a multi-class classifier assigns category labels to the remaining objects based on detailed regional features. This approach facilitates the optimal amalgamation of anchor-free proposal localization and category-based classification precision, significantly improving upon the limitations of prior one-stage systems such as CornerNet.

Evaluation and Numerical Results

The experimental evaluation of CPN centers on the challenging MS-COCO dataset. The numerical results substantiate the framework's effectiveness, with CPN achieving a mean average precision (AP) of 49.2% in multi-scale settings with a 104-layer stacked hourglass network. This denotes a performance enhancement over contemporary anchor-free frameworks such as CenterNet, showcasing an improvement of 2.2% in AP. These results affirm CPN's capability of detecting objects across diverse scales and peculiar aspect ratios more accurately than previous models.

Furthermore, CPN's design is emphasized to align with computational efficiency. For instance, with a DLA-34 backbone and no image flipping during inference, CPN maintains competitive precision, achieving APs of 41.6% and 39.7% at 26.2 and 43.3 FPS, respectively. Such efficiency makes CPN highly applicable to real-time object detection tasks, underscoring the advantage of its two-stage approach despite traditional perceptions of two-stage frameworks being slower.

Implications and Future Directions

The Corner Proposal Network offers a compelling synthesis of cutting-edge techniques in object detection. Its architectural design circumvents the trade-off between recall and precision that has often plagued object detection systems, proposing an efficient solution that doesn't compromise on accuracy. By improving both object proposal generation through an anchor-free approach and verification via a two-stage classification, CPN sets a new precedent in the development of object detection methodologies.

Looking forward, the significance of CPN is likely to spur further progress in AI-centric domains where robust and rapid object detection is vital, such as autonomous driving and AI surveillance systems. Future research can explore integrating more advanced backbone networks or leveraging additional contextual information to potentially raise the upper bounds of detection accuracy. Such enhancements will push the frontier even further, likely introducing new paradigms in dynamic, real-time image analysis.