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Lightweight express package detection with G-YOLO feature fusion
ID：162 View Protection：ATTENDEE Updated Time：2025-11-03 11:40:05 Hits：268 Poster Presentation

Start Time：2025-11-09 09:11（Asia/Shanghai）

Duration：1min

Session：P Poster presentation » P66.AI-driven technology

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Abstract
Addressing the challenges posed by low accuracy, a high false detection rate, and the need for lightweight solutions arising from susceptibility to noise and occlusion in complex scenarios, we propose an enhanced method for detecting express packages based on YOLOv5. To strike a more optimal balance between model performance and lightweight design, we introduce the GhostCTRBottleneck module. This module is designed to comprehensively capture feature dependencies while concurrently reducing computational overhead and parameter complexity. Our proposed method demonstrates a 2.5% increase in the MAP index compared to the original YOLOv5s, as demonstrated on a self-built express parcel dataset. Simultaneously, the model's weight, computational load, and parameter count are effectively reduced. Rigorous experiments conducted on the PASCAL VOC 2012 dataset underscore the efficacy and robustness of our method.
 
Keywords
YOLOv5, Express package detection, Lightweight, GhostCTRBottleneck
Speaker
Junfang Zhang
Yanching Institute of Technology

Submission Author
Junfang Zhang Yanching Institute of Technology
meng jiao wang Yanching Institute of Technology
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Important Date

  • Conference Date

    Nov 07

    2025

    to

    Nov 09

    2025

  • Oct 30 2025

    Draft paper submission deadline

  • Nov 10 2025

    Registration deadline

Sponsored By
IEEE西南交通大学IAS学生分会
Organized By
西南交通大学电气工程学院
SPACI车网关系研究室
四川大学电力系统稳定与高压直流输电研究团队
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