用迁移学习提升低分辨率热成像中人体关键点检测精度
Transfer Learning for Keypoint Detection in Low-Resolution Thermal TUG Test Images
- 采用MobileNetV3-Small+ViTPose架构，结合多任务损失优化特征对齐与热图精度
- 在TUG测试热图像上达到AP 0.861、AP50 0.942、AP75 0.887的高精度
- 模型参数少、计算量低，适合临床实时移动评估场景
本研究提出一种基于迁移学习的人体关键点检测新方法，用于低分辨率热成像中的运动能力评估。首次将时序起身走（Timed Up and Go, TUG）测试引入热成像计算机视觉领域，建立新型移动性评估范式。方法采用MobileNetV3-Small编码器与ViTPose解码器，通过复合损失函数平衡潜在表征对齐与热图准确性。模型在COCO关键点检测挑战的OKS指标下评估，取得AP 0.861、AP50 0.942、AP75 0.887的性能，优于传统监督学习方法如Mask R-CNN和ViTPose-Base。同时，模型在参数量和浮点运算次数（FLOPS）方面表现更优，具备更强计算效率。该研究为热成像在康复监测中的临床应用奠定基础。
原文摘要 · Abstract (English)
This study presents a novel approach to human keypoint detection in low-resolution thermal images using transfer learning techniques. We introduce the first application of the Timed Up and Go (TUG) test in thermal image computer vision, establishing a new paradigm for mobility assessment. Our method leverages a MobileNetV3-Small encoder and a ViTPose decoder, trained using a composite loss function that balances latent representation alignment and heatmap accuracy. The model was evaluated using the Object Keypoint Similarity (OKS) metric from the COCO Keypoint Detection Challenge. The proposed model achieves better performance with AP, AP50, and AP75 scores of 0.861, 0.942, and 0.887 respectively, outperforming traditional supervised learning approaches like Mask R-CNN and ViTPose-Base. Moreover, our model demonstrates superior computational efficiency in terms of parameter count and FLOPS. This research lays a solid foundation for future clinical applications of thermal imaging in mobility assessment and rehabilitation monitoring.
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