Appearance defect detection algorithm of substation instrument based on improved YOLOX

doi:10.11996/JG.j.2095-302X.2023050937

Abstract

Abstract:

In response to the wide distribution of defects, the variety of appearance defect features, and the challenges in extracting features during the substation instrument appearance defect detection task, the appearance defect detection algorithm of substation instrument based on improved YOLOX was proposed. Through the contrast sample strategy, the discriminative features of various appearance defects could be identified and extracted in depth. Additionally, the Global Context Feature Module was added to the network backbone to enhance the learning ability of the model for appearance defect features and improve the network performance. Finally, the SIoU loss function was integrated in the prediction part to fully consider the influence of the direction frame’s angle on model optimization, thereby improving the accuracy of detecting appearance defects in substation instrument and reducing the occurrence of missed detection and false detection. In the experiment, four typical types of appearance defects of substation instruments were selected as the experimental objects. The analysis of experimental results showed an average accuracy rate increase of 6.2% compared with the baseline model. This substantial improvement effectively enhanced the detection effect for appearance defects in substation instruments, thus creating favorable conditions for intelligent monitoring of unmanned substations.

Key words: substation instruments, appearance defect detection, YOLOX, contrast sample strategy, global context feature module, SIoU

CLC Number:

TP391

ZHAO Zhen-bing, MA Di-ya, SHI Ying, Li Gang. Appearance defect detection algorithm of substation instrument based on improved YOLOX[J]. Journal of Graphics, 2023, 44(5): 937-946.

Figures/Tables 10

References 23

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名称	型号
操作系统	Ubuntu18.04
CPU	I9-10900X
GPU	RTX A5000
CUDA	11.1
cuDNN	7.6.5
Python	3.7
深度学习框架	PyTorch 1.8.1

名称	型号
操作系统	Ubuntu18.04
CPU	I9-10900X
GPU	RTX A5000
CUDA	11.1
cuDNN	7.6.5
Python	3.7
深度学习框架	PyTorch 1.8.1

缺陷种类	图像数量	标签名称
表盘脏污	100	dirty
表盘破损	70	obscure
表盘凝露	72	condensation
示数不清晰	87	confuse
正常仪表	329	meter

缺陷种类	图像数量	标签名称
表盘脏污	100	dirty
表盘破损	70	obscure
表盘凝露	72	condensation
示数不清晰	87	confuse
正常仪表	329	meter

YOLOX	对比样本策略	全局上下文信息模块	SIoU损失函数	AP (%)				mAP (%)
YOLOX	对比样本策略	全局上下文信息模块	SIoU损失函数	表盘脏污	表盘破损	表盘凝露	示数不清晰	mAP (%)
√	-	-	-	91.2	84.2	84.3	76.7	84.1
√	√	-	-	90.8	83.5	91.3	83.6	87.3
√	-	√	-	91.4	87.9	84.7	84.5	87.1
√	-	-	√	89.8	89.4	85.9	83.3	87.1
√	√	√	√	92.4	87.0	96.3	85.4	90.3