LSR-YOLO:用于零售产品检测的轻量级快速模型
Yawen Zhao1, Mahmud Iwan Solihin1, Defu Yang2
1Faculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur, Malaysia.
PloS one
|October 22, 2025
概括
本研究介绍了LSR-YOLO,这是一款用于零售AI的轻量级物体检测模型. 它显著提高了推断速度,并降低了实时应用程序的计算成本.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 深度学习对象检测增强了零售产品的识别.
- 现有的方法面临着高计算成本和缓慢速度的挑战.
- 在智能城市和智能设备中需要高效的模型.
研究的目的:
- 提出LSR-YOLO,这是一个基于YOLOv8n的轻量级物体检测框架.
- 优化模型用于机器人和智能设备的部署.
- 提高推断速度,减少实时零售应用的计算负载.
主要方法:
- 开发了LSR-YOLO与架构优化,包括CSPHet-CBAM注意模块.
- 实现了通道修剪算法,以减少模型冗余.
- 在Locount和COCO数据集上评估性能.
主要成果:
- 在Locount数据集上,LSR-YOLO实现了357.1 FPS的推断速度.
- 该模型达到72.2%的mAP50和47.8%的mAP50-95.
- 与YOLOv8n相比,表现出246.7 FPS的增加,参数和GFLOP显著减少.
结论:
- LSR-YOLO为实时零售物体检测提供了卓越的准确性和计算效率.
- 该模型的轻量级设计和高速使其适用于资源有限的设备.
- 在COCO数据集上验证了概括能力,证实了其实际适用性.
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