BDD-DETR: An efficient algorithm for detecting small surface defects on lithium batteries

doi:10.19799/j.cnki.2095-4239.2024.0591

Abstract

Abstract:

To address the challenges posed by the large scale and shape differences of defects on the end face of lithium battery casings, which complicate the detection of small target defects, we introduce a novel lithium battery surface defect detection algorithm based on battery defects detection-detection transformer (BDD-DETR). The BDD-DETR framework introduces a new feature perception and fusion network (FPFN) module between the general feature extraction and detection head modules. Through the adaptive feature perception module and the feature fusion path in FPFN, the deep and shallow features of this network from multiple directions are merged, the response of crucial feature information is enhanced, and redundant features are suppressed, which further improves the ability of the model to fuse multi-scale features and its capability to detect small objects. In addition, to minimize distance and shape deviations during defect bounding box regression, the shape intersection over union loss function is employed to train the network model. Experimental results indicate that on a constructed lithium battery end surface defect dataset, compared to the collaborative-detection transformer, BDD-DETR improves average precision by 3.7%, small-scale object detection precision by 8.9%, and average recall rate by 1.1%. Furthermore, BDD-DETR outperforms several advanced object detection approaches in detecting small defects in lithium batteries.

Key words: lithium-ion battery, defect detection, Co-DETR, feature perception and fusion network, Shape IoU loss

CLC Number:

TP 391.41

Yuanxiu XING, Zhuanwei LIU, Yufeng XING, Wenbo WANG. BDD-DETR: An efficient algorithm for detecting small surface defects on lithium batteries[J]. Energy Storage Science and Technology, 2025, 14(1): 370-379.

Figures/Tables 15

Fig.1

Fig.2

Table 1

Table 2

Performance comparison of BDD-DETR with other detectors"

Method	Backbone	$m A P 50$	$m A P s$	$m A P m$	$m A P L$	$A R$
Faster RCNN $[7]$	Res50	55.2	0.0	21.3	41.0	42.9
Cascade RCNN $[8]$	Res50	68.0	2.6	23.0	41.8	37.7
YOLOv5-L	CSPDarkNet	63.9	19.1	25.0	39.4	48.7
YOLOv7-L $[10]$	CSPDarkNet	51.2	17.0	29.6	25.3	43.5
YOLOv8-L	CSPDarkNet	45.0	17.1	18.1	29.8	37.2
Conditional DETR $[15]$	Res50	55.2	11.5	12.0	33.0	35.4
DINO $[16]$	Res50	73.8	18.5	24.5	49.8	55.6
CenterNet $[22]$	Res50	56.0	4.1	20.6	49.8	43.1
Co-DETR $[17]$	Res50	74.1	12.6	25.1	49.1	57.9
BDD-DETR(ours)	Res50	77.8	21.5	26.0	51.6	59.0

Table 2

Table 3

Ablation study of the subclass division"

Method	$m A P 50$
亚类划分前	65.8
亚类划分后	70.5

Table 3

Table 4

Ablation study of the AFP module"

SRU+CRU	CBAM	$m A P 50$	$m A P S$	$A R$
×	×	72.7	20.2	56.5
√	×	75.9(+3.2)	21.2(+1.0)	59.0(+2.5)
×	√	74.6(+1.9)	10.6(-9.6)	54.9(-1.6)
√	√	77.8(+5.1)	21.5(+1.3)	59.0(+2.5)

Table 4

Table 5

Ablation study of the SRU and CRU components"

Item	$m A P 50$	$m A P S$	$A R$
*	74.6	10.6	54.9
SRU	74.1(-0.5)	20.6(+10.0)	54.1(-0.8)
CRU	75.5(+0.9)	21.3(+10.7)	53.0(-1.9)
CRU+SRU	73.2(-1.4)	17.3(+6.7)	57.7(+2.8)
SRU+CRU	77.8(+3.2)	21.5(+10.9)	59.0(+4.1)

Table 5

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Fig.9

Table 6

The impact of Shape IoU loss and GIoU loss on model performance"

Method	$m A P 50$	$m A P S$	$m A P M$	$m A P L$	$A R$
GIoU	72.0	12.9	27.8	54.3	56.3
Shape IoU	77.8	21.5	26.0	51.6	59.0

Table 6

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