ST-YOLO:基于红外热成像和机器视觉技术的光伏模块的缺陷检测方法
Hanfei Xie1,2, Baoxi Yuan1,2, Chengyu Hu1,2
1School of Electronic Information, Xijing University, Xi 'an, Shaanxi, China.
PloS one
|December 12, 2024
概括
一个新的算法,ST-YOLO,通过提高准确性和速度,同时减少模型大小来提高光伏面板缺陷检测. 这为识别太阳能系统缺陷提供了更有效的解决方案.
科学领域:
- 可再生能源系统可再生能源系统
- 在工程领域的人工智能.
- 材料科学 材料科学 材料科学
背景情况:
- 光伏 (PV) 面板的质量对于发电效率和系统安全至关重要.
- 传统的缺陷检测方法面临的挑战包括高成本,低可靠性和缓慢的速度.
研究的目的:
- 开发一个先进的算法,以高效准确的光伏模块缺陷检测.
- 克服现有的缺陷检测技术的局限性.
主要方法:
- 基于YOLOv8s的提议ST-YOLO算法,包含C2f-SCconv和三重注意力机制.
- 利用光伏阵列的自制红外缺陷图像数据集进行实验.
主要成果:
- 与YOLOv8s相比,ST-YOLO的模型重量减少了15%,精度提高了2.9%,mAP@0.5增加了1.4%.
- 在光伏缺陷检测方面,其表现优于YOLOv7-Tiny和YOLOv5s.
结论:
- ST-YOLO为光伏模块的检测速度和准确性提供了显著的改进.
- 该算法显示了可靠和高效的光伏缺陷检测的实质应用价值.
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