基于深度学习的太阳能电池板缺陷检测技术研究进展
Q3 Engineering
引用次数: 0
摘要
简介：基于机器视觉技术开展光伏面板缺陷检测技术研究，解决光伏面板生产线自动化在线缺陷检测与定位难题。目标：目标是提高光伏电池生产线缺陷检测的准确性，提高缺陷检测速度以满足实时监控需求，提高生产效率。方法：本文讨论了基于图像处理的检测、基于传统机器学习的检测和基于深度学习算法的检测等三种检测方法，并分别进行了比较和分析。最后得出结论，基于深度学习的检测方法相比之下更有效。然后，通过几种基于深度学习的检测算法做了进一步的分析和模拟实验。结果：实验结果表明，YOLOv8 算法的精确率最高，在召回率和 mAP 值方面也保持了良好的结果。检测速度均低于其他算法，为 10.6ms。结论：基于 yolov8 算法的检测模型具有最高的综合性能，是最适合检测生产线上太阳能电池板缺陷的算法模型。本文章由计算机程序翻译，如有差异，请以英文原文为准。
Research Progress on Deep Learning Based Defect Detection Technology for Solar Panels
INTRODUCTION: Based on machine vision technology to carry out photovoltaic panel defect detection technology research to solve the photovoltaic panel production line automation online defect detection and localization problems.
OBJECTIVES: The goal is to improve the accuracy of defect detection on PV cell production lines, increase the speed of defect detection to meet real-time monitoring needs, and improve production efficiency.
METHODS: In this paper, three detection methods such as image processing based detection, traditional machine learning based detection and deep learning algorithm based detection are discussed and compared and analyzed respectively. Finally, it is concluded that deep learning based detection methods are more effective in comparison. Then, further analysis and simulation experiments are done by several deep learning based detection algorithms.
RESULTS: The experimental results show that the YOLOv8 algorithm has the highest precision rate and maintains good results in terms of recall and mAP values. The detection speed is all less than other algorithms, 10.6ms.
CONCLUSION: The inspection model based on yolov8 algorithm has the highest comprehensive performance and is the most suitable algorithmic model for detecting defects in solar panels in production lines.
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来源期刊
EAI Endorsed Transactions on Energy Web
Energy-Energy Engineering and Power Technology
CiteScore
2.60
自引率
0.00%
发文量
14
审稿时长
10 weeks
期刊介绍：
With ICT pervading everyday objects and infrastructures, the ‘Future Internet’ is envisioned to undergo a radical transformation from how we know it today (a mere communication highway) into a vast hybrid network seamlessly integrating knowledge, people and machines into techno-social ecosystems whose behaviour transcends the boundaries of today’s engineering science. As the internet of things continues to grow, billions and trillions of data bytes need to be moved, stored and shared. The energy thus consumed and the climate impact of data centers are increasing dramatically, thereby becoming significant contributors to global warming and climate change. As reported recently, the combined electricity consumption of the world’s data centers has already exceeded that of some of the world''s top ten economies. In the ensuing process of integrating traditional and renewable energy, monitoring and managing various energy sources, and processing and transferring technological information through various channels, IT will undoubtedly play an ever-increasing and central role. Several technologies are currently racing to production to meet this challenge, from ‘smart dust’ to hybrid networks capable of controlling the emergence of dependable and reliable green and energy-efficient ecosystems – which we generically term the ‘energy web’ – calling for major paradigm shifts highly disruptive of the ways the energy sector functions today. The EAI Transactions on Energy Web are positioned at the forefront of these efforts and provide a forum for the most forward-looking, state-of-the-art research bringing together the cross section of IT and Energy communities. The journal will publish original works reporting on prominent advances that challenge traditional thinking.
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