Research on improvement strategy of DETR real-time object detection algorithm for small devices

Chao Gao; Jing Gao; Lili Cao; Long Zhao; Shaojie Gao

doi:10.1117/12.3055829

9 January 2025 Research on improvement strategy of DETR real-time object detection algorithm for small devices

Chao Gao, Jing Gao, Lili Cao, Long Zhao, Shaojie Gao

Author Affiliations +

Proceedings Volume 13486, Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024); 134861C (2025) https://doi.org/10.1117/12.3055829
Event: Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024), 2024, Chengdu, China

Abstract

With the rapid development of deep learning technology, object detection, as an important task in the field of computer vision, has also been widely applied in the ROS(Robot Operating System) robot field. ROS, as an open-source robot operating platform, provides a favorable operating environment for object detection algorithms. This article studies the improvement strategy of real-time object detection algorithm (RT-DETR) for small devices, which provides a better solution for the field of robotics. Through the application analysis of the RT-DETR algorithm, we found that there are certain issues with detection accuracy and efficiency, as well as the ability to detect small objects, when the performance of small devices is limited. Especially when the camera is not in focus or the image is ghosted, the issues of detection accuracy and efficiency are particularly prominent. For this purpose, we propose a dynamic and irregular deformable convolution kernel strategy to address the performance issues of small edge devices in terms of detection accuracy. In response to efficiency concerns, we propose an enhanced non-linear network structure to achieve greater non-linear capability with fewer parameters applied during the operation process. Finally, we combine the two methods to form a DenNet network (Deformable and Enhanced Nonlinear Convolutional Kernel Networks). Through experimental verification, our improvement strategy can greatly improve the detection accuracy and efficiency of small devices, solve the efficiency problem of insufficient performance of small devices, and has important practical application value.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Chao Gao, Jing Gao, Lili Cao, Long Zhao, and Shaojie Gao "Research on improvement strategy of DETR real-time object detection algorithm for small devices", Proc. SPIE 13486, Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024), 134861C (9 January 2025); https://doi.org/10.1117/12.3055829

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KEYWORDS

Object detection

Convolution

Transformers

Education and training

Detection and tracking algorithms

Visual process modeling

Data modeling

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