【摘要】 [目的]为解决传统表面缺陷检测方法存在的速度慢、效率低、终端设备计算资源有限等问题，[方法]对基于改进YOLOv8s的热轧带钢表面缺陷检测方法进行分析。[结果]研究表明：相较于原始YOLOv8s模型，改进的YOLOv8s模型的检测精度与模型轻量化程度都得到了显著提升；相较于目前主流的表面缺陷检测算法，改进的YOLOv8s模型在热轧带钢表面缺陷检测中可有效提高小目标检测能力，其轻量化模型有利于在移动端或嵌入式设备上进行部署。[结论]研究成果可为热轧带钢表面缺陷检测提供一定参考。更多还原

【Abstract】 [Purpose] To address the problems of slow speed, low efficiency, and limited computational resources of traditional surface defect detection methods, [Method] the surface defect detection method for hot-rolled strip steel based on improved YOLOv8s is analyzed. [Result] Research shows that compared with the original YOLOv8s model, the improved YOLOv8s model significantly improves the detection accuracy and model lightweighting; Compared with the current mainstream surface defect detection algorithms, the improved YOLOv8s model can effectively improve the small target detection capability in the surface defect detection of hot-rolled steel strip. Its lightweight model is suitable for use in mobile or embedded devices. [Conclusion] The research results can provide some references for the surface defect detection of hot-rolled strip steel.更多还原