报告详情
Lightweight express package detection with G-YOLO feature fusion
编号：162
访问权限：仅限参会人
更新：2025-11-03 11:40:05
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张贴报告
报告开始：2025年11月09日 09:11（Asia/Shanghai）
报告时间：1min
所在会场：[P] Poster presentation [P6] 6.AI-driven technology
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摘要
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.
关键词
YOLOv5, Express package detection, Lightweight, GhostCTRBottleneck
报告人
Junfang Zhang
Yanching Institute of Technology稿件作者
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重要日期
-
会议日期
11月07日
2025
至11月09日
2025
-
10月30日 2025
初稿截稿日期
-
11月10日 2025
注册截止日期
主办单位
IEEE西南交通大学IAS学生分会
承办单位
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
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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