YOLOX-Ray:一个高效的基于注意力的单阶段物体探测器,适用于工业检查
António Raimundo1,2, João Pedro Pavia1,3, Pedro Sebastião1,2
1Instituto de Telecomunicações (IT), Av. Rovisco Pais, 1, 1049-001 Lisboa, Portugal.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
一种新的深度学习模型YOLOX-Ray通过改进特征提取和小物体检测来增强工业检查. 这种人工智能驱动的方法为各行各业提供了更有效和高效的质量和安全评估.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 工业工程 工业工程 工业工程
背景情况:
- 工业检查对于质量和安全至关重要.
- 深度学习模型对检查任务有希望.
- 现有的方法可能在工业环境中难以实现多尺度物体检测.
研究的目的:
- 推出YOLOX-Ray,这是一个高效的深度学习架构,用于工业检查.
- 为了增强特征提取和小规模物体检测能力.
- 评估YOLOX-Ray在现实世界工业检查场景中的性能.
主要方法:
- 基于YOLO物体检测的YOLOX-Ray架构.
- 在FPN和PAN中整合SimAM注意力机制.
- 利用Alpha-IoU成本函数用于小物体检测.
主要成果:
- 在热点,裂和腐蚀检测方面,YOLOX-Ray获得了高mAP50得分 (89%至99.6%).
- 对于具有挑战性的小物体,mAP50:95度 (44.7%至66.1%) 的显著性能.
- 对比分析证实了SimAM和Alpha-IoU的协同效益.
结论:
- 对于工业检查而言,YOLOX-Ray在多尺度物体检测方面表现出卓越的性能.
- 合并的SimAM注意力和Alpha-IoU损失对于最佳结果至关重要.
- YOLOX-Ray可实现更有效,高效和可持续的工业检查流程.
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