对比YOLO系列模型在网页验证码检测中的表现，找最优平衡点。
Benchmarking of Different YOLO Models for CAPTCHAs Detection and Classification
- 用YOLOv5/v8/v10的n/s/m版本检测网页验证码，多数据集验证
- nano版最快，m版精度最高，推理速度与准确率有权衡
- 提出图像切片法提升大图检测效果，适合快速适配新验证码
本文针对网页验证码检测任务，分析并比较了YOLOv5、YOLOv8和YOLOv10模型在真实网页数据、Darknet数据集及合成网页数据上的表现。研究涵盖纳米（n）、小（s）和中等（m）三种规模的YOLO架构，采用精确率（Precision）、召回率（Recall）、F1分数、mAP@50和推理速度等指标评估实际应用价值。此外，考察了训练好的模型对新验证码模式的快速适应能力。针对网页中常见的大尺寸输入图像，提出图像切片方法以提升检测性能。结果表明：nano版本在推理速度上表现最佳，而更复杂的m版本在其他指标上更具优势。
原文摘要 · Abstract (English)
This paper provides an analysis and comparison of the YOLOv5, YOLOv8 and YOLOv10 models for webpage CAPTCHAs detection using the datasets collected from the web and darknet as well as synthetized data of webpages. The study examines the nano (n), small (s), and medium (m) variants of YOLO architectures and use metrics such as Precision, Recall, F1 score, mAP@50 and inference speed to determine the real-life utility. Additionally, the possibility of tuning the trained model to detect new CAPTCHA patterns efficiently was examined as it is a crucial part of real-life applications. The image slicing method was proposed as a way to improve the metrics of detection on oversized input images which can be a common scenario in webpages analysis. Models in version nano achieved the best results in terms of speed, while more complexed architectures scored better in terms of other metrics.
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