Surface Defect Detection of Power Equipment Using Adaptive Receptive Field Network

doi:10.16182/j.issn1004731x.joss.22-0278

Abstract

Abstract:

For the detection of defects such as icing, rust, and contamination of power equipment in substations, a novel adaptive receptive field network (ARFN) is proposed,in which an adaptive receptive field module (ARFM) combined with the attention mechanism can effectively fuse multi-scale features. Considering the small sample learning attribute of defect detection, a power equipment surface defect simulation data synthesis method based on real texture is also proposed. The experimental results on the simulation dataset show that the network has high detection accuracy for surface defects across devices, while having advantages such as small size and fast operation speed.

Key words: surface defect detection, adaptive receptive field, attention mechanism, multi-scale feature, simulation data synthesis

CLC Number:

TP391.9

Hao Yu, Jinxia Jiang, Xiaohan Lai, Feng Mei, Qing Wang. Surface Defect Detection of Power Equipment Using Adaptive Receptive Field Network[J]. Journal of System Simulation, 2023, 35(7): 1572-1580.

Figures/Tables 6

Fig. 1

Table 1

Network Parameters

网络层次	ARFN-small	ARFN-medium	ARFN-large
FC, Softmax
Conv1	7×7, 16, s=2	7×7, 32, s=2	7×7, 64, s=2
Pooling	3×3, max pooling, s=2
Layer1	$1 × 1 161 3 × 3 46 1 × 1 161$	$1 × 1 321 3 × 3 86 1 × 1 321$	$1 × 1 641 3 × 3 166 1 × 1 641$
Layer2	$1 × 1 321 3 × 3 86 1 × 1 321$	$1 × 1 641 3 × 3 166 1 × 1 641$	$1 × 1 1281 3 × 3 326 1 × 1 1281$
Layer3	$1 × 1 641 3 × 3 166 1 × 1 641$	$1 × 1 1281 3 × 3 326 1 × 1 1281$	$1 × 1 2561 3 × 3 646 1 × 1 2561$
Layer4	$1 × 1 1281 3 × 3 326 1 × 1 1281$	$1 × 1 2561 3 × 3 646 1 × 1 2561$	$1 × 1 5121 3 × 3 1286 1 × 1 5121$

Table 1

Table 2

Table 3

Fig. 2

Table 4

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网络	覆冰	锈蚀	污秽	mAP
VGG16	0.171	0.133	0.177	0.160
ResNet50	0.909	0.908	0.906	0.907
ResNet101	0.909	0.908	0.908	0.908
ResNet50-fpn	0.914	0.913	0.913	0.913
ResNet50-afpn	0.914	0.914	0.913	0.914
ARFN-small	0.891	0.837	0.817	0.849
ARFN-medium	0.907	0.895	0.898	0.900
ARFN-large	0.909	0.908	0.907	0.908

网络	模型大小/MB	计算复杂度/Gflops	参数数量(M)
VGG16	163.63	192.79	24.22
ResNet50	182.23	264.49	28.30
ResNet101	327.35	300.62	47.29
ResNet50-fpn	217.32	320.29	31.53
ResNet50-afpn	200.78	323.67	32.44
ARFN-small	4.28	5.89	0.60
ARFN-medium	10.26	22.12	1.44
ARFN-large	28.56	85.65	3.97

缺陷类型	ARFN_s/w	ARFN_s/o
mAP	0.849	0.810
覆冰	0.891	0.873
锈蚀	0.837	0.796
污秽	0.817	0.760

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