Insulator Defect Detection of Lightweight Rotating YOLOv5 Based on Adaptive Feature Fusion

IF 0.6 Q4 AUTOMATION & CONTROL SYSTEMS
Jiang Xiang Ju,  Wang Rui Tong
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引用次数: 0

Abstract

With the construction of smart grid, aerial insulator defect detection based on computer vision has become an important task to ensure grid safety. When the target detection model is too large, it is not conducive to the edge deployment of aerial inspection UAV; Moreover, different aerial photography angles and distances will cause the insulator string in the image to have any direction and less defect information. In order to solve these problems, this paper proposes a rotating GBS-AFP-YOLOv5 model with the combination of lightweight and adaptive features. Firstly, an improved YOLOv5 based on lightweight GBS is proposed by Ghost convolution, which can effectively extract features while reducing the complexity of the model. Then, an adaptive information interaction feature pyramid (AFP) is proposed by combining CARAFE upsampling operator and ECA attention, which effectively fuses the feature information of shallow and deep defects and improves the model performance. Then, a more accurate insulator string detection method is realized by using rotating frame combined with ring label smoothing technology. Finally, the normalized wasserstein distance (NWD) is introduced to improve the loss function, which further enhances the detection ability of the model for small targets with defects. Based on the insulator data set, the test results show that the model has a good defect detection performance, which is improved from mAP0.5 to 0.923 on the basis of only 4.32M parameters.

Abstract Image

基于自适应特征融合的轻型旋转 YOLOv5 绝缘子缺陷检测
随着智能电网的建设,基于计算机视觉的绝缘子缺陷空中检测已成为保障电网安全的重要任务。当目标检测模型过大时,不利于航检无人机的边缘部署;而且不同的航拍角度和距离会导致图像中绝缘子串的方向不一,缺陷信息较少。为了解决这些问题,本文提出了一种结合轻量级和自适应特征的旋转 GBS-AFP-YOLOv5 模型。首先,通过 Ghost 卷积提出了基于轻量级 GBS 的改进 YOLOv5,在降低模型复杂度的同时有效提取特征。然后,结合 CARAFE 上采样算子和 ECA 注意,提出了自适应信息交互特征金字塔(AFP),有效融合了浅缺陷和深缺陷的特征信息,提高了模型性能。然后,利用旋转框架结合环标平滑技术,实现了更精确的绝缘子串检测方法。最后,引入归一化韦塞尔斯坦距离(NWD)来改进损失函数,进一步提高了模型对小目标缺陷的检测能力。基于绝缘体数据集的测试结果表明,该模型具有良好的缺陷检测性能,在仅有 4.32M 个参数的基础上,缺陷检测性能从 mAP0.5 提高到了 0.923。
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来源期刊
AUTOMATIC CONTROL AND COMPUTER SCIENCES
AUTOMATIC CONTROL AND COMPUTER SCIENCES AUTOMATION & CONTROL SYSTEMS-
CiteScore
1.70
自引率
22.20%
发文量
47
期刊介绍: Automatic Control and Computer Sciences is a peer reviewed journal that publishes articles on• Control systems, cyber-physical system, real-time systems, robotics, smart sensors, embedded intelligence • Network information technologies, information security, statistical methods of data processing, distributed artificial intelligence, complex systems modeling, knowledge representation, processing and management • Signal and image processing, machine learning, machine perception, computer vision
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