摘要
机械零件视觉识别在工业自动化的快速发展中对提高生产线效率和产品质量控制至关重要。本研究聚焦于优化YOLOv8算法在机械零件视觉识别上的表现,通过实施照明自适应调节机制,解决了传统深度学习模型在钢板表面缺陷检测中因光照不均导致的漏检误检问题。通过深入分析YOLOv8算法,设计照明自适应调节机制,构建了完整的视觉识别系统。通过实验收集不同照明条件下的机械零件图像,比较照明自适应调节对系统性能的影响。该机制大幅提升了机械零件视觉识别准确率与效率,对工业自动化领域发展具有重要意义。
The visual recognition of mechanical parts is crucial for improving production line efficiency and product quality control in the rapid development of industrial automation.This study focuses on optimizing the performance of the YOLOv8 algorithm in visual recognition of mechanical parts,missed detection and false detection caused by uneven illumination in traditional deep learning model for surface defect detection of steel plate is solved,by implementing an adaptive lighting adjustment mechanism.By deeply analyzing the YOLOv8 algorithm,an adaptive lighting adjustment mechanism is designed to build a complete visual recognition system.Collect images of mechanical parts under different lighting conditions through experiments and compare the impact of adaptive lighting adjustment on system performance.This mechanism significantly improves the accuracy and efficiency of visual recognition for mechanical parts,which is of great importance to the development of industrial automation.
作者
王艺森
WANG Yisen(Jilin Agricultural University,Changchun 130118,P.R.China)
出处
《灯与照明》
2025年第6期10-12,共3页
Light & Lighting
关键词
照明自适应调节
YOLOv8
机械零件
视觉识别
adaptive lighting adjustment
YOLOv8
mechanical components
visual recognition
