Improved Surface Defects Detection Algorithm for Aluminum Profiles Based on PP-PicoDet-XS

doi:10.12068/j.issn.1005-3026.2024.11.005

Abstract

Abstract:

During the production and processing of aluminum profiles， multiple types of surface defects such as unclear features and varying scales may generate. In response to the problems of low accuracy， poor real?time performance， and strong subjectivity in existing manual sampling method， an improved surface defects detection algorithm is proposed for aluminum profiles based on PP-PicoDet-XS.The SimAM attention was embedded in the backbone to enhance the ability of extracting deep effective features. The SIoU（Scylla intersection over union） loss function is used to optimize the training process to improve the positioning ability of the prediction boxes. The quantization and distillation were used to compress the model to improve the inference speed. The results show that the improved algorithm achieves a mean average precision of 98.93% at intersection over union（IoU） threshold of 0.5， and 57.60% across IoU thresholds ranging from 0.5 to 0.95， which is 1.73% and 4.13% higher than the uncompressed original model. Deploying this algorithm on the Snapdragon 865 mobile platform for inference， the inference speed can reach 116.82 frames per second， which is 47 frames per second higher than the uncompressed original model.

Key words: aluminum profiles, defects detection, SimAM, loss function, quantization, distillation

CLC Number:

TP 391.4

Shu-hua MA, Li-zhen LI, Han-min QIN, Xiao-peng SHA. Improved Surface Defects Detection Algorithm for Aluminum Profiles Based on PP-PicoDet-XS[J]. Journal of Northeastern University(Natural Science), 2024, 45(11): 1557-1564.

Figures/Tables 11

References 24

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主干网络组成	算子	卷积核尺寸	步长	SE
卷积块	卷积	3×3	2	—
DSC模块_1	DSC	3×3	1	—
DSC模块_2	DSC	3×3	2	—
DSC模块_2	DSC	3×3	1	—
DSC模块_3	DSC	3×3	2	—
DSC模块_3	DSC	3×3	1	—
DSC模块_4	DSC	3×3	2	—
DSC模块_4	DSC×5	5×5	1	—
DSC模块_5	DSC	5×5	2	√
DSC模块_5	DSC	5×5	1	√

主干网络组成	算子	卷积核尺寸	步长	SE
卷积块	卷积	3×3	2	—
DSC模块_1	DSC	3×3	1	—
DSC模块_2	DSC	3×3	2	—
DSC模块_2	DSC	3×3	1	—
DSC模块_3	DSC	3×3	2	—
DSC模块_3	DSC	3×3	1	—
DSC模块_4	DSC	3×3	2	—
DSC模块_4	DSC×5	5×5	1	—
DSC模块_5	DSC	5×5	2	√
DSC模块_5	DSC	5×5	1	√

网络模型	SimAM 位置	mAP@0.5∶0.95	mAP@0.5
网络模型	SimAM 位置	%	%
PP-PicoDet- XS	1 0 0 0 0	54.51	98.47
	0 1 0 0 0	54.12	98.02
	0 0 1 0 0	53.48	97.51
	0 0 0 1 0	53.55	97.22
	0 0 0 0 1	55.56	98.81
	1 1 1 1 1	53.66	97.94

网络模型	SimAM 位置	mAP@0.5∶0.95	mAP@0.5
网络模型	SimAM 位置	%	%
PP-PicoDet- XS	1 0 0 0 0	54.51	98.47
	0 1 0 0 0	54.12	98.02
	0 0 1 0 0	53.48	97.51
	0 0 0 1 0	53.55	97.22
	0 0 0 0 1	55.56	98.81
	1 1 1 1 1	53.66	97.94

网络模型	IoU 损失函数	mAP@0.5∶0.95	mAP@0.5
网络模型	IoU 损失函数	%	%
PP-PicoDet-XS	GIoU	53.47	97.20
	DIoU	54.16	97.73
	CIoU	54.06	97.89
	SIoU	56.22	98.72