改进YOLOv8n的轻量型蜂窝陶瓷缺陷检测算法
Improved YOLOv8n Lightweight Honeycomb Ceramic Defect-Detection Algorithm
胡海宁 1黄雷阳 2杨洪刚 2陈云霞3
作者信息
- 1. 上海第二工业大学智能制造与控制工程学院,上海 201209;上海电机学院机械学院,上海 201306
- 2. 上海电机学院机械学院,上海 201306
- 3. 上海第二工业大学智能制造与控制工程学院,上海 201209
- 折叠
摘要
为了解决YOLO模型在圆柱形蜂窝陶瓷缺陷图像检测上精度不足、参数量大等问题,提出了一种基于改进YOLOv8n的轻量化圆柱形蜂窝陶瓷缺陷检测算法.针对裂纹缺陷的边界定位模糊问题,采用Shape-IoU优化边界框回归,通过权重系数和形状损失项提升定位准确性.同时,为了提高对低分辨率小目标裂纹的识别能力,引入高效多尺度注意力(EMA)机制,以增强网络对特征信息的捕获与提取.此外,算法在backbone中集成了改进的SCConv方法,减少了参数冗余,并在此基础上设计了空间和通道特征融合金字塔模块来实现网络模型的轻量化.改进后的网络相比于原网络,平均预测精确度提高了2.9百分点,参数量减少到了原网络的84.1%,每秒帧数提高了9帧,而且模型更轻量化、模型运算量更少、更利于模型的实际部署和嵌入式使用.
Abstract
To solve the insufficient detection accuracy of the YOLO model and the numerous parameters required for detecting defects in cylindrical honeycomb ceramic images,a lightweight,cylindrical honeycomb ceramic defect-detection algorithm based on the improved YOLOv8n is proposed.To mitigate fuzzy boundary localization for crack defects,Shape-IoU is used to optimize bounding-box regression and improve the localization accuracy via weight coefficients and shape-loss terms.Meanwhile,to enhance the recognition ability of low-resolution small target cracks,an efficient multiscale attention(EMA)mechanism is introduced to enhance the network's capture and extraction of feature information.The algorithm integrates an improved SCConv module in the backbone to reduce parameter redundancy.Based on this,a space and channel feature fusion pyramid module is designed to achieve a lightweight network model.Compared with the original network,the improved network offers a higher average prediction accuracy by 2.9 percentage points,a lower parameter count by 84.1%,and an increase in the number of frames per second by 9 frames.Additionally,the proposed model is lighter and features a smaller computational load,which is more conducive to actual model deployment and embedded use.
关键词
缺陷检测/YOLOv8n/注意力机制/特征融合/轻量化模型Key words
defect-detection/YOLOv8n/attention mechanism/feature fusion/lightweight model引用本文复制引用
出版年
2024
NETL
NSTL
万方数据