基于多尺度融合和改进YOLOv8n的光伏缺陷检测方法

doi:10.3969/j.issn.2097-0706.2024.11.004

综合智慧能源 ›› 2024, Vol. 46 ›› Issue (11): 29-37.doi: 10.3969/j.issn.2097-0706.2024.11.004

• 人工智能赋能的运维与巡检 • 上一篇下一篇

基于多尺度融合和改进YOLOv8n的光伏缺陷检测方法

张文强¹^,²(), 李加树¹^,², 宣洋¹^,²^,^*(), 李辰¹^,², 钱杭¹^,², 张啸宇¹^,²

1.安徽大学人工智能学院，合肥 230601
2.自主无人系统技术教育部工程研究中心，合肥 230601

收稿日期:2024-08-05 修回日期:2024-09-05 出版日期:2024-11-25
通讯作者: * 宣洋（2000），男，硕士生，科研助理，从事基于深度学习的无人机巡检输电线路方面的研究，wa22301056@stu.ahu.edu.cn。
作者简介:张文强（2004），男，科研助理，从事计算机视觉目标检测方面的研究，wa2224079@stu.ahu.edu.cn。
基金资助:
国家自然科学基金项目(62303005);安徽省大学生创新创业训练计划项目(S202410357259);安徽大学校级质量工程项目(2024XJZLGC164);安徽大学校级质量工程项目(2024XJZLGC168)

Defect detection method of PV panels based on multi-scale fusion and improved YOLOv8n

ZHANG Wenqiang¹^,²(), LI Jiashu¹^,², XUAN Yang¹^,²^,^*(), LI Chen¹^,², QIAN Hang¹^,², ZHANG Xiaoyu¹^,²

1. School of Artificial Intelligence，Anhui University，Hefei 230601，China
2. Engineering Research Center of Autonomous Unmanned System Technology， Ministry of Education， Hefei 230601， China

Received:2024-08-05 Revised:2024-09-05 Published:2024-11-25
Supported by:
National Natural Science Foundation of China Project(62303005);Anhui Province College Students Innovation and Entrepreneurship Training Program(S202410357259);Anhui University Quality Engineering Project(2024XJZLGC164);Anhui University Quality Engineering Project(2024XJZLGC168)

摘要/Abstract

摘要：

光伏发电在新型能源系统中扮演着重要的角色，其稳定运行对于保障能源供应至关重要。然而，光伏板在实际应用中面临着复杂的外部环境，如紫外线辐射、腐蚀、受潮等，可能会导致光伏板组件出现裂纹、断栅等问题，进而影响其功能。针对目前光伏板缺陷检测算法存在的模型复杂度较高、小目标缺陷检测的精度不高等问题，提出了一种基于改进YOLOv8n的光伏缺陷检测算法。重新设计了一种新的主干网络，减少模型的参数量，提出C2f高效局部注意力（C2f-Efficient Local Attention，C2f-ELA）模块，提高模型对细微特征的定位能力，提出加权双向特征金字塔网络（Weight-Bidirectional Feature Pyramid Network，W-BiFPN）更换原有的网络结构并融合P2小目标检测层，有效提高了模型捕获多尺度特征的能力，同时利用浅层网络的信息来加强局部特征感知，对小目标的识别精度得到显著提升。试验结果表明：该方法相较于基础YOLOv8算法，参数量下降了16.7%，平均精度提升了3.1百分点，验证了改进算法在检测性能上的提升。

关键词: 缺陷检测, 深度学习, YOLOv8n, 轻量化模块, 高效局部注意力, 加权双向特征金字塔网络

Abstract:

Photovoltaic power generation plays a critical role in modern energy systems， and its stable operation is essential for ensuring energy supply. However， photovoltaic panels are exposed to complex environmental factors， such as ultraviolet radiation， corrosion， moisture， which can lead to defects like cracks and broken grids， thereby potentially compromising their functionality. To address the problems of high model complexity and low detection accuracy for small defects in current photovoltaic panel defect detection algorithms， a defect detection algorithm based on improved YOLOv8n was proposed. A new backbone network was designed to reduce the model's parameters and computation； The C2F-Efficient Local Attention （C2F-ELA） module was introduced to enhance the model's capability to precisely localize subtle features. Weight-Bidirectional Feature Pyramid Network （W-BiFPN） was then proposed to replace the original network structure and integrate the P2 small target detection layer. This effectively enhanced the model's ability to capture multi-scale features while leveraging shallow network information to strengthen the local feature perception， boosting the accuracy of small target recognition. Experimental results showed that， compared to the baseline YOLOv8 algorithm， this method reduced the number of parameters by 16.7% and increased the mean average precision by 3.1 percentage points， demonstrating an improvement in detection performance.

Key words: defect detection, deep learning, YOLOv8n, lightweight module, ELA, W-BiFPN

中图分类号:

TK01：TP391.41

张文强, 李加树, 宣洋, 李辰, 钱杭, 张啸宇. 基于多尺度融合和改进YOLOv8n的光伏缺陷检测方法[J]. 综合智慧能源, 2024, 46(11): 29-37.

ZHANG Wenqiang, LI Jiashu, XUAN Yang, LI Chen, QIAN Hang, ZHANG Xiaoyu. Defect detection method of PV panels based on multi-scale fusion and improved YOLOv8n[J]. Integrated Intelligent Energy, 2024, 46(11): 29-37.

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方法	参数量/10⁶	准确率/%	召回率/%	F₂值	mAP_0.5/%
YOLOv8n	3.0	88.4	81.8	0.850	88.8
GhostConv	2.8	82.0	83.0	0.825	88.1
C2f-ELA	3.0	86.0	85.6	0.858	89.1
W-BiFPN	2.7	89.5	84.2	0.868	89.8
GhostConv+C2f-ELA	2.8	85.7	82.8	0.842	90.1
本文	2.5	93.0	83.8	0.882	91.9

模型	F₂值	mAP_0.5/%
C2f-ELA_A	0.838	88.9
C2f-ELA_N	0.836	84.4
C2f-ELA_B	0.858	89.1

模型	参数量/10⁶	计算量/GFLOPs	准确率/%	召回率/%	F₂值	mAP_0.5/%
文献[25]	4.2	18.1	85.9	82.3	0.841	88.1
文献[26]	13.3	31.6	88.6	86.1	0.873	88.4
Faster RCNN	137.1	370.2	39.3	88.3	0.544	65.2
YOLOv3	61.5	155.3	85.6	88.6	0.871	89.7
YOLOv5m	21.1	25.2	86.4	87.2	0.868	89.3
YOLOv7	37.2	150.1	87.8	79.1	0.832	85.7
YOLOv9m	32.6	130.7	85.1	89.2	0.871	90.2
YOLOv10m	16.5	64.0	84.9	85.0	0.849	88.6
本文	2.5	11.7	93.0	83.8	0.882	91.9

基于多尺度融合和改进YOLOv8n的光伏缺陷检测方法

Defect detection method of PV panels based on multi-scale fusion and improved YOLOv8n

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