Laser & Optoelectronics Progress, Volume. 62, Issue 8, 0815002(2025)

Lightweight Small Object Detection Algorithm Based on STD-DETR

Zeyu Yin1,*... Bo Yang2, Jinling Chen1, Chuangchuang Zhu1, Hongli Chen3 and Jin Tao1 |Show fewer author(s)
Author Affiliations
  • 1School of Electrical Engineering and Information, Southwest Petroleum University, Chengdu 610500, Sichuan , China
  • 2State Grid Sichuan Information & Telecommunication Company, Chengdu 610095, Sichuan , China
  • 3Petroleum Engineering School, Southwest Petroleum University, Chengdu 610500, Sichuan , China
  • show less

    To address the challenges of small target detection in aerial photography images by unmanned aerial vehicle, including complex background, tiny and dense targets, and difficulties in deploying models on mobile devices, this paper proposes an improved lightweight small target detection algorithm based on real-time DEtection TRansformer (RT-DETR) model, named STD-DETR. First, RepConv is introduced to improve the lightweight Starnet network, replacing the original backbone network, thereby achieving lightweight. A novel feature pyramid is then designed, incorporating a 160 pixel × 160 pixel feature map output at the P2 layer to enrich small target information. This approach replaces the traditional method of adding a P2 small target detection head, and introduces the CSP-ommiKernel-squeeze-excitation (COSE) module and space-to-depth (SPD) convolution to enhance the extraction of global features and the fusion of multi-scale features. Finally, pixel intersection over union (PIoU) is used to replace the original model's loss function, calculating IoU at the pixel level to more precisely capture small overlapping regions, reducing the miss rate and improving detection accuracy. Experimental results demonstrate that, compared with baseline model, the STD-DETR model achieves improvements of 1.3 percentage points, 2.2 percentage points, and 2.3 percentage points in accuracy, recall, and mAP50 on the VisDrone2019 dataset, while reducing computational cost and parameters by ~34.0% and ~37.9%, respectively. Generalization tests on the Tinyperson dataset show increases of 3.7 percentage points in accuracy and 3.1 percentage points in mAP50, confirming the model's effectiveness and generalization capability.

    Keywords
    Tools

    Get Citation

    Copy Citation Text

    Zeyu Yin, Bo Yang, Jinling Chen, Chuangchuang Zhu, Hongli Chen, Jin Tao. Lightweight Small Object Detection Algorithm Based on STD-DETR[J]. Laser & Optoelectronics Progress, 2025, 62(8): 0815002

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category: Machine Vision

    Received: Aug. 15, 2024

    Accepted: Sep. 23, 2024

    Published Online: Mar. 24, 2025

    The Author Email:

    DOI:10.3788/LOP241849

    CSTR:32186.14.LOP241849

    Topics