DynamicDet: A Unified Dynamic Architecture for Object Detection (2304.05552v1)

Abstract: Dynamic neural network is an emerging research topic in deep learning. With adaptive inference, dynamic models can achieve remarkable accuracy and computational efficiency. However, it is challenging to design a powerful dynamic detector, because of no suitable dynamic architecture and exiting criterion for object detection. To tackle these difficulties, we propose a dynamic framework for object detection, named DynamicDet. Firstly, we carefully design a dynamic architecture based on the nature of the object detection task. Then, we propose an adaptive router to analyze the multi-scale information and to decide the inference route automatically. We also present a novel optimization strategy with an exiting criterion based on the detection losses for our dynamic detectors. Last, we present a variable-speed inference strategy, which helps to realize a wide range of accuracy-speed trade-offs with only one dynamic detector. Extensive experiments conducted on the COCO benchmark demonstrate that the proposed DynamicDet achieves new state-of-the-art accuracy-speed trade-offs. For instance, with comparable accuracy, the inference speed of our dynamic detector Dy-YOLOv7-W6 surpasses YOLOv7-E6 by 12%, YOLOv7-D6 by 17%, and YOLOv7-E6E by 39%. The code is available at https://github.com/VDIGPKU/DynamicDet.

• The paper presents a unified dynamic architecture that adapts inference pathways based on the multi-scale challenges of object detection.
• It introduces an adaptive router and novel optimization strategy that boost detection speed by up to 39% on benchmarks like COCO.
• The framework balances computational efficiency with accuracy, enabling versatile applications in areas such as autonomous driving and video surveillance.

Overview of DynamicDet: A Unified Dynamic Architecture for Object Detection

The paper "DynamicDet: A Unified Dynamic Architecture for Object Detection" addresses the emerging challenge of designing a dynamic neural network specifically tailored for object detection tasks. The proposed architecture, DynamicDet, is characterized by its adaptability and efficiency, leveraging a unified dynamic approach informed by object detection requirements. This architecture capitalizes on adaptive inference mechanisms to achieve notable improvements in both accuracy and computational efficiency.

DynamicDet introduces a dynamic framework encapsulating several innovative contributions:

1. Dynamic Architecture Design: The architecture is specifically devised to handle the unique demands of object detection, which often involves multi-scale processing due to the varied sizes and categories of detected objects within a single image.
2. Adaptive Router: Central to the model is an adaptive router, which intelligently analyzes multi-scale information and autonomously determines the optimal inference pathway for each image. This component reflects the human brain's ability to variably process information based on contextual complexity.
3. Novel Optimization Strategy: The framework includes an exiting criterion rooted in detection losses. This criterion aids in the optimization of detection performance, balancing between computational load and accuracy.
4. Variable-Speed Inference Strategy: A highlight of the system is its ability to perform variable-speed inference. This feature enables the trade-off between processing speed and detection accuracy using a single dynamic detector, facilitating wider applicability across diverse scenarios.

Empirical results underscore the efficacy of DynamicDet on the COCO benchmark, showcasing state-of-the-art accuracy-speed trade-offs. For example, the Dy-YOLOv7-W6 configuration significantly surpasses the performance of existing YOLOv7 models across varying accuracy thresholds, achieving up to a 39% increase in inference speed at comparable accuracy levels.

Numerical Results and Robust Claims

• The paper reports that DynamicDet's configuration, Dy-YOLOv7-W6, offers a 12% speed enhancement compared to YOLOv7-E6, a 17% improvement over YOLOv7-D6, and a considerable 39% over YOLOv7-E6E, all while maintaining competitive accuracy.
• These results are afforded by the architecture's strategic design, which reduces redundant computations and focuses processing power on images deemed complex by the adaptive router.

Implications and Future Directions

The implications of DynamicDet extend across both theoretical and practical domains. Theoretically, the framework enriches dynamic neural network paradigms by providing a specialized infrastructure for object detection, a task distinct from image classification in its complexity and requirements. Practically, DynamicDet enhances real-time object detection applications, such as autonomous driving and video surveillance, where speed and accuracy are paramount.

Future developments in AI based on DynamicDet's principles may explore the integration of even more nuanced adaptive mechanisms, potentially informed by feedback loops simulating even closer human cognitive processes. Additionally, the approach could be generalized further across other computer vision tasks, ensuring broader utility and facilitating the development of robust, comprehensive AI systems adaptable to myriad operational environments.

Overall, this research delineates a path toward more efficient, adaptable object detection frameworks, encouraging further exploration and enhancement of dynamic neural network constructs.