煤质技术2025，Vol.40Issue(2)：85-90,96,7.DOI:10.3969/j.issn.1007-7677.2025.02.012
基于轻量级YOLOv8的坩埚缺陷识别算法
Crucible defect recognition based on lightweight YOLOv8
摘要
Abstract
To address the challenges of low detection reliability in robotic intelligent laboratory systems caused by sig-nificant geometric diversity of crucibles and imbalanced defect sample distribution,this paper proposes a crucible de-fect detection method based on the lightweight YOLOv8.Firstly,an image data augmentation strategy using Denoising Diffusion Probabilistic Model(DDPM)is introduced to balance and expand crucible image data according to the dis-tribution of data labels.Subsequently,to better balance the inference speed and detection accuracy of the network model,an improved YOLOv8 crucible defect detection algorithm is proposed,incorporating a non-parametric atten-tion mechanism and an adaptive weighting scheme.Experimental results demonstrate that the proposed method a-chieves a mean Average Precision(mAP)of 97.40%with an inference time of 1.20 ms per image.Although the in-ference speed is 0.2 ms slower than YOLOv11-n,the detection accuracy is optimal among current general object de-tection methods,especially lightweight ones.The proposed method can be effectively applied in industrial crucible defect detection scenarios.关键词
坩埚缺陷识别算法/机器人智能化验系统/YOLOv8/数据扩充/目标检测/加权机制/推理时间Key words
crucible defect recognition algorithm/robotic intelligent laboratory system/YOLOv8/data augmenta-tion/object detection/weighted mechanism/inference time分类
化学工程引用本文复制引用
罗彬彬,庞晴蔚,康希平..基于轻量级YOLOv8的坩埚缺陷识别算法[J].煤质技术,2025,40(2):85-90,96,7.基金项目
岳麓山工业创新中心衡山实验室科研基金资助项目(HST2312) （HST2312）
