一个基于YOLO NAS的优化框架,用于实时对象检测
Chhaya Gupta1,2, Nasib Singh Gill1, Preeti Gulia3
1Department of Computer Science and Applications, Maharshi Dayanand University, Rohtak, India.
Scientific reports
|September 25, 2025
概括
这项研究通过使用MISH激活和人工蜂群 (ABC) 优化来增强YOLO-NAS对象检测模型. 改进的模型实现了卓越的精度,回忆和平均平均精度 (mAP),用于实时对象识别.
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:
- 对象检测模型对于各种AI应用至关重要.
- 现有的YOLO-NAS变种需要进一步优化以提高性能.
- 集成先进的激活功能和优化算法可以提高模型的稳定性和准确性.
研究的目的:
- 通过结合MISH激活和人工蜂群 (ABC) 优化来增强YOLO-NAS对象检测模型.
- 评估增强的YOLO-NAS模型与基线YOLO-NAS变体和其他最先进模型的性能.
- 为了证明将生物灵感优化与高级激活功能相结合的有效性,以改善对象检测.
主要方法:
- 整合MISH激活功能,以改善特征表示和梯度流.
- 应用人工蜂群 (ABC) 优化算法用于超参数调整.
- 在定制数据集上测试增强的YOLO-NAS模型,并将其性能指标 (精度,回忆,mAP) 与YOLOv6,YOLOv7和YOLOv8.8进行比较.
主要成果:
- 与基线YOLO-NAS变体相比,增强的YOLO-NAS模型在精度,回忆和平均精度 (mAP) 上表现出卓越的性能.
- 拟议的模型在准确性,回忆,精度,F1得分和mAP方面超过了YOLOv6,YOLOv7和YOLOv8在不同交叉点和联合 (IoU) 值 (0.50,0.75,0.95) 上.
- 微调的模型在实时对象识别任务中实现了惊人的98%的准确性.
结论:
- 结合MISH激活和ABC优化,可以显著提高YOLO-NAS模型的训练稳定性和预测准确性.
- 拟议的微调的YOLO-NAS模型代表了实时物体检测的最先进方法.
- 这项研究强调了将生物灵感优化器与现代激活功能集成在一起,以提高计算机视觉的潜力.
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