基于改进的YOLOv8s网络模型的X射线安全图像中的走私检测方案
Qingji Gao1, Haozhi Deng1, Gaowei Zhang2
1Robotics Institute, Civil Aviation University of China, Tianjin 300300, China.
Sensors (Basel, Switzerland)
|February 24, 2024
概括
这项研究引入了一个改进的YOLOv8s走私物品检测算法,YOLOv8s-DCN-EMA-IPIO*,提高了公共交通安全的准确性. 这种新的方法提高了检测率,减少了错过和虚假的走私物品识别.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 运输安全 运输安全
背景情况:
- 对公共交通安全而言,X射线检查至关重要,但目前的走私物品检测方法在准确性方面面临挑战,包括错过和错误检测.
- 提高深度学习模型在复杂场景中对象检测的性能是一个正在进行的研究领域.
研究的目的:
- 开发一个先进的走私物品检测算法,YOLOv8s-DCN-EMA-IPIO*,以提高X射线安全查的准确性和可靠性.
- 解决当前检测模型的局限性,如背景噪音和遮蔽,以提高公共安全.
主要方法:
- 拟议的YOLOv8s-DCN-EMA-IPIO*算法集成了使用SRGAN的超分辨率重建,脊柱中的可变形卷积 (DCNv2),高效的多尺度注意力 (EMA) 机制以及改进的子灵感优化 (IPIO) 算法.
- 通过超分辨率和架构增强的数据增强旨在改善特征提取,强度和噪声/遮蔽处理.
主要成果:
- 在自建数据集上,YOLOv8s-DCN-EMA-IPIO*模型实现了73.43%的平均平均精度 (mAP),比原始YOLOv8s的性能提高了3.98%.
- 改进的模型显示每秒 (FPS) 率为95,表明实时处理能力.
结论:
- YOLOv8s-DCN-EMA-IPIO*算法显著提高了走私物品检测的准确性,并保持了实时性能.
- 这种增强的模型为公共交通中更有效的安全查提供了有希望的解决方案.
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