YOLO8 Performance for different weights.

Accurate eye detection in thermal images is essential for diverse applications, including biometrics, healthcare, driver monitoring, and human-computer interaction. However, achieving this accuracy is often hindered by the inherent limitations of thermal data, such as low resolution and poor contrast. This work addresses these challenges by proposing a novel, multifaceted approach that combines both deep learning and image processing techniques. We first introduce a unique dataset of thermal facial images captured with meticulous eye location annotations. To improve image clarity, we employ Contrast Limited Adaptive Histogram Equalization (CLAHE). Subsequently, we explore the effectiveness of advanced YOLO models (YOLOv8 and YOLOv9) for accurate eye detection. Our experiments reveal that YOLOv8 with CLAHE-enhanced images achieved the highest accuracy (precision and recall of 1, mAP50 of 0.995, and mAP50-95 of 0.801), the YOLOv9 model also demonstrated excellent performance with a precision of 0.998, recall of 0.998, mAP-50 of 0.995, and mAP50-95 of 0.753. Furthermore, to enhance the resolution of detected eye regions, we investigate various super-resolution techniques, ranging from traditional methods like Bicubic interpolation to cutting-edge approaches like generative adversarial networks (BSRGAN, ESRGAN) and advanced models like Real-ESRGAN, SwinIR, and SwinIR-Large with ResShift. The performance of these techniques is evaluated using both objective and subjective quality measures. Overall, this work demonstrates the effectiveness of our proposed pipeline, which seamlessly integrates image enhancement, deep learning, and super-resolution techniques. This synergic fusion significantly improves the contrast, accuracy of eye detection, and overall resolution of thermal images, paving the way for potential applications across various fields.红外热成像图像中的精准眼部检测，在生物识别、医疗健康、驾驶员监控以及人机交互等诸多领域均具备关键应用价值。然而，热红外数据固有的局限性（如分辨率低下、对比度不足）往往会阻碍该精准检测效果的实现。本研究针对上述挑战，提出了一种融合深度学习与图像处理技术的新型多维度解决方案。首先，我们构建了一套独特的热红外人脸图像数据集，其中包含精细标注的眼部位置信息。为提升图像清晰度，我们采用了对比度受限自适应直方图均衡化（Contrast Limited Adaptive Histogram Equalization）技术。随后，我们针对先进的YOLO模型（YOLOv8与YOLOv9）在精准眼部检测任务中的有效性展开了探索。实验结果表明，搭配CLAHE增强图像的YOLOv8模型取得了最优检测精度：精确率与召回率均为1，mAP50为0.995，mAP50-95为0.801；YOLOv9模型同样表现出色，其精确率为0.998、召回率为0.998，mAP50为0.995，mAP50-95为0.753。此外，为提升检测到的眼部区域的分辨率，我们研究了多种超分辨率技术，涵盖双三次插值（Bicubic interpolation）这类传统方法，以及生成对抗网络（BSRGAN、ESRGAN）等前沿方案，还包括Real-ESRGAN、SwinIR以及搭载ResShift的SwinIR-Large等先进模型。我们通过客观与主观质量评价指标，对上述技术的性能进行了评估。总体而言，本研究验证了所提出的集成图像增强、深度学习与超分辨率技术的完整流程的有效性。这种协同融合方案可有效提升热红外图像的对比度、眼部检测精度与整体分辨率，为其在各领域的潜在应用铺平了道路。