基于光度立体和深度学习的电池缺陷检测方法

doi:10.13229/j.cnki.jdxbgxb.20230130

Abstract

Abstract:

Aiming at the problem that the battery defect detection is susceptible to black appearance interference， which leads to the limited detect identification of the observation image under a single light source， an end-to-end deep-learning photometric stereo network （DPSNet） is proposed. Firstly， the photometric stereo feature generator （PSFG） enables model to facilitate transformation from multi-input features to normal feature so that better excel in defect signals. Then channel co-attention （CCA） module explores the channel interactions between each input and surface normal for informative representations， and fuses modify features to coordinately enhance global representation. Finally， spatial pyramid pooling （SPP） and feature pyramid networks （FPN） achieve multi-scale prediction. The experimental results on the self-built Battery101 dataset show that the proposed method achieves better effect. In addition， the ablation experiment further verifies the effectiveness of each module in the model.

Key words: signal and information processing, battery defect detection, photometric stereo, channel co-attention

CLC Number:

TP391.4

Yu-ting SU,Meng-yao JING,Pei-guang JING,Xian-yi LIU. Deep photometric stereo learning framework for battery defect detection[J].Journal of Jilin University(Engineering and Technology Edition), 2024, 54(12): 3653-3659.

Figures/Tables 6

Fig.1

Fig.2

Table 1

Effect of different parameter values on model performance"

评估指标	$λ$
评估指标	0.01	0.1	1	10	100
mAP/%	87.2	87.6	89.6	86.4	84.3
方差	2.08	1.61	1.44	1.47	5.94

Table 1

Fig.3

Table 2

Table 3

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方法	骨干网络	缺陷类别						mAP/%	FPS/（f·s^-1）
方法	骨干网络	耳朵	气泡	标签	褶皱	变形	划痕	mAP/%	FPS/（f·s^-1）
SSD300	ResNet-50	76.9	95.8	99.9	53.9	100.0	0	71.1	173.4
YOLOv3	Darknet-53	89.4	97.6	99.9	85.1	100.0	3.2	79.6	193.6
LAD	ResNet-50	78.5	94.4	99.9	64.5	99.9	40.4	79.6	258.3
VFNet	ResNet-50	79.8	97.4	99.9	71.1	100.0	32.6	80.1	254.2
LD	ResNet-50	77.5	100.0	99.9	77.9	100.0	27.7	80.5	182.4
YOLOX	Darknet-53	85.6	100.0	99.9	81.7	100.0	22.8	81.0	252.0
RetinaNet	ResNet-50	92.2	100.0	99.9	56.6	94.4	40.1	82.2	535.7
Mask2Former	ResNet-50	78.2	100.0	99.9	85.1	100.0	39.6	83.8	103.3
Faster RCNN	ResNet-50	92.2	97.6	99.9	93.6	100.0	43.4	87.8	37.0
本文	Darknet-53	99.2	100.0	99.9	93.1	100.0	45.6	89.6	42.6

PSFG	CCA	SPP	mAP
		√	83.6
√			82.7
√	√		84.0
√		√	86.2
√	√	√	89.6

Deep photometric stereo learning framework for battery defect detection

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