MatrixNets: A New Scale and Aspect Ratio Aware Architecture for Object Detection (2001.03194v1)

Abstract: We present MatrixNets (xNets), a new deep architecture for object detection. xNets map objects with similar sizes and aspect ratios into many specialized layers, allowing xNets to provide a scale and aspect ratio aware architecture. We leverage xNets to enhance single-stage object detection frameworks. First, we apply xNets on anchor-based object detection, for which we predict object centers and regress the top-left and bottom-right corners. Second, we use MatrixNets for corner-based object detection by predicting top-left and bottom-right corners. Each corner predicts the center location of the object. We also enhance corner-based detection by replacing the embedding layer with center regression. Our final architecture achieves mAP of 47.8 on MS COCO, which is higher than its CornerNet counterpart by +5.6 mAP while also closing the gap between single-stage and two-stage detectors. The code is available at https://github.com/arashwan/matrixnet.

• The paper presents MatrixNets, a novel CNN architecture that assigns objects to specific layers based on scale and aspect ratio.
• It demonstrates substantial improvements by achieving up to 47.8 mAP on the MS COCO dataset, outperforming traditional FPN methods.
• The approach enhances computational efficiency and versatility, narrowing the performance gap between single-stage and two-stage detectors.

MatrixNets: A Novel Architecture for Scale and Aspect Ratio Aware Object Detection

The development of robust object detection architectures continues to be a pivotal area of research in computer vision, driven by numerous practical applications such as object tracking, instance segmentation, and image captioning. The paper "MatrixNets: A New Scale and Aspect Ratio Aware Architecture for Object Detection" offers an innovative solution to address the ongoing challenges associated with detecting objects of varying scales and aspect ratios.

Overview of MatrixNets

The proposed approach, MatrixNets (denoted as $x$Nets in the paper), introduces a novel convolutional neural network (CNN) architecture tailored for object detection. MatrixNets differentiate themselves by specifically mapping objects based on their sizes and aspect ratios to distinct layers, effectively making the architecture both scale and aspect ratio aware. This distinct feature allows MatrixNets to outperform traditional architectures like Feature Pyramid Networks (FPNs), which, although scale-invariant, manage aspect ratios inadequately.

Architecture and Implementation

MatrixNets employ a matrix of layers, where each matrix layer is assigned objects of particular scales and aspect ratios, which is a fundamental shift from the straightforward hierarchical mapping of FPNs. The diagonal layers of the matrix capture objects with square-like aspect ratios, akin to FPN layers with varying receptive fields, while off-diagonal layers are specifically designed to address objects with extreme aspect ratios. This architectural design enables MatrixNets to use a consistent square convolutional kernel across different layers without losing information, thus improving detection accuracy.

MatrixNets are versatile and applicable to various object detection frameworks. They have been successfully integrated into anchor-based, single-stage detectors, as well as corner-based methods, such as those inspired by CornerNet and CenterNet. Notably, the integration with CornerNet presented an opportunity to replace computationally expensive corner pooling layers with standard convolutions, thereby enhancing computational efficiency and detection accuracy.

Empirical Results

The empirical results of the paper demonstrate that MatrixNets provide substantial improvements over existing single-stage object detection architectures. Specifically, the paper reports that MatrixNets achieve a mean Average Precision (mAP) of up to 47.8 on the MS COCO dataset, a notable improvement of 5.6 mAP over the equivalent CornerNet architecture. Additionally, this architecture narrows the performance gap between single-stage and two-stage detectors, traditionally seen as superior in precision.

Implications and Future Directions

The implications of MatrixNets are multifaceted. Practically, these advancements allow for more efficient and accurate object detection in real-world applications. Theoretically, MatrixNets provide an insightful approach to designing CNN architectures that inherently consider scale and aspect ratio variability.

Future research could explore extending MatrixNets to two-stage detectors and other computer vision tasks beyond object detection, such as instance segmentation and key-point detection. Moreover, investigating the computational trade-offs and real-time processing capabilities in edge computing environments could significantly broaden the applicability of MatrixNets in real-time applications.

Conclusion

MatrixNets signify a substantial step forward in crafting object detection frameworks that are proficient in managing the diversity of object scales and aspect ratios. By reimagining the conventional pyramid-like architecture, MatrixNets have successfully demonstrated enhanced performance and applicability, setting a new benchmark for future research endeavors in the field of computer vision.