摘要
为了实现低分辨率绝缘子红外图像的高精度检测,提出了基于类内稀疏先验和改进YOLOv8的红外多尺度绝缘子检测方法。针对绝缘子图像存在局部密集导致漏检的问题,提出了通用性的类内稀疏先验,将先验知识和训练数据结合,使模型感知绝缘子独有的几何特征和形态信息,可以无计算代价地提升目标检测模型的精度,并提供一种绝缘子数据样本的规范化标注方法;针对红外图像分辨率低导致特征提取困难的问题,将鲁棒特征下采样模块替代卷积下采样,保留细粒度细节信息,增强关键特征图的鲁棒表示;针对绝缘子尺度变化大且存在目标遮挡的问题,设计了wise-MPDIoU改进边框损失函数,以改善模型对不同尺寸绝缘子的定位能力。实验数据表明,相比于基线模型,在AP50和AP50:95指标上分别提升3.3和3.5个百分点,为绝缘子热像检测提供了新的方案。
To achieve high-precision detection of low-resolution thermal image of insulators,an infrared multi-scale insulator detection method was proposed based on intra-class sparse prior and improved YOLOv8.To address the issue of missed detections due to local density in insulator images,a universal intra-class sparse prior was proposed,combining prior knowledge with training data.This enabled the model to perceive the unique geometric features and morphological information of the insulator,enhancing the accuracy of the target detection model without additional computational cost,and provided a standardized annotation method for insulator data samples.To tackle the difficulty of feature extraction from low-resolution infrared images,a robust feature downsampling module was employed to replace convolutional downsampling,preserving fine-grained detail information and enhancing the robust representation of key feature maps.For the problem of large-scale variations and occlusion in insulator targets,a wise-MPDIoU was utilized,improving the bounding-box loss function and the model’s ability to localize insulators of different sizes.Experimental data demonstrated that,compared to the baseline model,the proposed method achieved improvements of 3.3 and 3.5 percentage points in AP50 and AP50:95 metrics,respectively,providing a new solution for insulator thermal image detection.
作者
赵振兵
欧阳文斌
冯烁
李浩鹏
马隽
ZHAO Zhenbing;Ouyang Wenbin;FENG Shuo;LI Haopeng;MA Jun(Department of Electronic and Communication Engineering,North China Electric Power University,Baoding Hebei 071003,China;Hebei Key Laboratory of Power Internet of Things Technology,North China Electric Power University,Baoding Hebei 071003,China;Engineering Research Center of Intelligent Computing for Complex Energy Systems,Ministry of Education,North China Electric Power University,Baoding Hebei 071003,China;Stem Cell Research Center,Human Anatomy Department,Hebei Medical University,Shijiazhuang Hebei 050017,China)
出处
《图学学报》
北大核心
2025年第6期1247-1256,共10页
Journal of Graphics
基金
国家自然科学基金(61871182,U21A20486,62373151,62371188,62303184)
河北省自然科学基金(F2021502008,F2021502013)
中央高校基本科研业务费专项资金(2023JC006)。
关键词
绝缘子
红外图像
类内稀疏先验
目标检测
深度学习
insulator
thermal image
within-class sparse prior knowledge
object detection
deep learning
