摘要
YOLO目标检测算法是当前基于图像的输电线路绝缘子缺陷检测的主流方法,然而现有模型复杂度较大,亟需合理有效的参数压缩方法作为前提条件,来为解决无人机边缘设备部署的困境问题奠定基础;同时,无人机航拍的绝缘子缺陷图像背景复杂、缺陷尺寸较小,容易出现误检、漏检等问题。为此,提出了一种用于输电线路绝缘子多缺陷检测的Insulator Defect Detection-YOLOv7(IDD-YOLOv7)模型,以降低模型复杂度,提高模型鲁棒性。首先,在多尺度特征融合的过程中加入坐标注意力(Coordinate Attention)机制,抑制复杂背景的干扰,提升模型对小目标的全局感知能力;之后,设计C3GhostNetV2模块,用于捕获不同空间像素之间的远程依赖性,在增强模型表达能力的同时降低模型的参数量和浮点运算量;最后,提出Focal-CIoU损失函数,提高模型高质量anchor的贡献,加快模型的收敛速度。实验结果表明,本文方法与基线模型相比mAP^(50)提升了3.8%,查准率和召回率分别提升了1.7%和7.6%,参数量和浮点运算量分别下降了18.3%和14.0%,绝缘子自爆、破损、闪络缺陷的AP^(50)分别提升了0.8%、4.5%、6.3%。
The YOLO objective detection algorithm is currently the mainstream method for detecting insulator defects in image-based power transmission lines.However,due to the high complexity of existing models,a reasonable and effective parameter compression method is urgently needed as a prerequisite to establish the foundation for solving the dilemma of UAV edge device deployment.Additionally,the complex background of the insulator defect images captured by drones and small size of defects can lead to problems such as false detections and omissions.To address these issues,the Insulator Defect Detection-YOLOv7(IDD-YOLOv7)model was proposed for multi-defect detection in power transmission line insulators,aiming to reduce model complexity and enhance robustness.Firstly,a coordinate attention mechanism was incorporated during the multi-scale feature fusion process to suppress interference from complex backgrounds and enhance the model’s global perception of small objects.Secondly,a C3GhostNetV2 module was designed to capture long-range dependencies between different spatial pixels,thus enhancing the model’s expressive power while reducing the parameter quantity and floating-point operation complexity.Lastly,the Focal-CIoU loss function was proposed to improve the contribution of high-quality anchors to the model and accelerate model convergence.Experimental results demonstrated that compared with the baseline model,the mAP^(50)of this method has increased by 3.8%,with precision and recall rates increasing by 1.7%and 7.6%,respectively,and the parameter quantity and floating-point operations have decreased by 18.3%and 14.0%,respectively.The AP^(50)of insulator self-explosion,damage,and flashover defects have increased by 0.8%,4.5%,and 6.3%,respectively.
作者
翟永杰
赵晓瑜
王璐瑶
王亚茹
宋晓轲
朱浩硕
ZHAI Yongjie;ZHAO Xiaoyu;WANG Luyao;WANG Yaru;SONG Xiaoke;ZHU Haoshuo(Department of Automation,North China Electric Power University,Baoding Hebei 071003,China)
出处
《图学学报》
CSCD
北大核心
2024年第1期90-101,共12页
Journal of Graphics
基金
国家自然科学基金联合基金重点支持项目(U21A20486)
中央高校基本科研业务费专项资金项目(2023JC006)
国家自然科学基金青年基金项目(62303184)
河北省自然科学基金青年科学基金项目(F2021502008)。
