Lightweight large-format tile defect detection algorithm based on improved YOLOv8

doi:10.11772/j.issn.1001-9081.2024020198

Journal of Computer Applications ›› 2025, Vol. 45 ›› Issue (2): 647-654.DOI: 10.11772/j.issn.1001-9081.2024020198

• Multimedia computing and computer simulation • Previous Articles

Lightweight large-format tile defect detection algorithm based on improved YOLOv8

Songsen YU(), Zhifan LIN, Guopeng XUE, Jianyu XU

School of Software，South China Normal University，Foshan Guangdong 528225，China

Received:2024-02-28 Revised:2024-06-08 Accepted:2024-06-17 Online:2024-07-19 Published:2025-02-10
Contact: Songsen YU
About author:LIN Zhifan， born in 2001， M. S. candidate. His research interests include computer vision.
XUE Guopeng， born in 1999， M. S. candidate. His research interests include computer vision.
XU Jianyu， born in 2001， M. S. candidate. His research interests include computer vision.
Supported by:
Regional Joint Fund for Basic and Applied Basic Research Fund of Guangdong Province （Key Project）(2020B1515120089)

基于改进YOLOv8的轻量级大幅面瓷砖缺陷检测算法

余松森(), 林智凡, 薛国鹏, 徐建宇

华南师范大学软件学院，广东佛山 528225

通讯作者: 余松森
作者简介:林智凡（2001—），男，广东汕尾人，硕士研究生，主要研究方向：计算机视觉
薛国鹏（1999—），男，江西赣州人，硕士研究生，主要研究方向：计算机视觉
徐建宇（2001—），男，江西上饶人，硕士研究生，主要研究方向：计算机视觉。
基金资助:
广东省基础与应用基础研究基金省市联合基金重点项目(2020B1515120089)

Abstract

Abstract:

In view of the problems of current tile defect detection mainly relying on manual detection， such as strong subjectivity， low efficiency， and high labor intensity， an improved lightweight algorithm for detecting small defects in large-format ceramic tile images based on YOLOv8 was proposed. Firstly， the high-resolution large-format image was cropped， and HorBlock was introduced into the backbone network to enhance model’s capture capability. Secondly， Large Separable Kernel Attention （LSKA） was incorporated to improve C2f for improving the detection performance of the model and model’s feature extraction capability was enhanced by introducing SA （Shuffle Attention）. Finally， Omni-Dimensional Dynamic Convolution （ODConv） was introduced to further enhance model’s capability to handle with small defects. Experimental results on Alibaba Tianchi tile defect detection dataset show that the improved model not only has lower parameters than the original YOLOv8n， but also has an increase of 8.2 percentage points in mAP@0.5 and an increase of 7 percentage points in F1 score compared to the original YOLOv8n. It can be seen that the improved model can identify and process small surface defects of large-format tiles more accurately， and improve the detection effect significantly while maintaining lightweight.

Key words: tile defect detection, small object detection, YOLOv8, deep learning, lightweight

摘要：

针对当前瓷砖缺陷检测主要依靠人工检测导致的主观性强、效率低、劳动强度大等问题，提出一种基于改进YOLOv8的轻量级大幅面瓷砖图像微小缺陷检测算法。首先，对高分辨率大幅面图像进行裁切处理，并在骨干网络中引入HorBlock增强模型的捕捉能力；其次，融入大型可分离内核注意力（LSKA）改进C2f提高模型的检测性能，并通过引入SA（Shuffle Attention）增强模型的特征提取能力；最后，引入全维度动态卷积（ODConv）进一步增强模型对微小缺陷的处理能力。在阿里天池瓷砖瑕疵检测数据集上的实验结果表明：改进后的模型不仅参数量比原始YOLOv8n低，而且mAP@0.5提升了8.2个百分点，F1分数提升了7个百分点。可见，改进后的模型能更精确地识别和处理大幅面瓷砖的微小表面缺陷，且能在保持轻量级的同时，显著提升检测效果。

关键词: 瓷砖缺陷检测, 小目标检测, YOLOv8, 深度学习, 轻量级

CLC Number:

TP391.41

Songsen YU, Zhifan LIN, Guopeng XUE, Jianyu XU. Lightweight large-format tile defect detection algorithm based on improved YOLOv8[J]. Journal of Computer Applications, 2025, 45(2): 647-654.

余松森, 林智凡, 薛国鹏, 徐建宇. 基于改进YOLOv8的轻量级大幅面瓷砖缺陷检测算法[J]. 《计算机应用》唯一官方网站, 2025, 45(2): 647-654.

Figures/Tables 14

References 29

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缺陷类别	mAP@0.5	mAP@0.5：0.95
所有	74.5	42.7
边异常（edge）	91.9	59.5
角异常（corner）	94.5	64.0
白色点（white）	53.7	21.2
浅色块（gray）	56.7	27.7
深色点（black）	66.9	29.7
光圈（circle）	83.2	54.2

缺陷类别	mAP@0.5	mAP@0.5：0.95
所有	74.5	42.7
边异常（edge）	91.9	59.5
角异常（corner）	94.5	64.0
白色点（white）	53.7	21.2
浅色块（gray）	56.7	27.7
深色点（black）	66.9	29.7
光圈（circle）	83.2	54.2

模型	mAP@0.5/%	map@0.5：0.95/%	Params/10⁶	GFLOPs	F1/%	FPS
YOLOv8n	74.5	42.7	3.0	8.1	71	435
+HorBLock	79.0	45.1	3.1	9.2	75	286
+HorBLock+LSKA	80.1	45.2	2.8	8.9	76	263
+HorBLock+LSKA+SA	81.9	45.9	2.8	8.9	78	278
+HorBLock+LSKA+SA+ODConv	82.7	46.8	2.8	8.5	78	270

模型	mAP@0.5/%	map@0.5：0.95/%	Params/10⁶	GFLOPs	F1/%	FPS
YOLOv8n	74.5	42.7	3.0	8.1	71	435
+HorBLock	79.0	45.1	3.1	9.2	75	286
+HorBLock+LSKA	80.1	45.2	2.8	8.9	76	263
+HorBLock+LSKA+SA	81.9	45.9	2.8	8.9	78	278
+HorBLock+LSKA+SA+ODConv	82.7	46.8	2.8	8.5	78	270

模型	mAP@0.5/%	map@0.5：0.95/%	Params/10⁶	GFLOPs	F1/%	FPS
YOLOv3-tiny	74.6	42.3	12.1	18.9	71	455
YOLOv5n	76.5	40.4	2.5	7.1	72	455
YOLOv5s	81.5	45.6	9.1	23.8	77	323
YOLOv6n	65.7	36.2	4.2	11.8	64	476
YOLOv7-tiny	39.1	14.0	6.0	13.1	46	161
YOLOv8s	83.5	49.3	11.1	28.4	79	323
YOLOv8-MobileNetV3	72.8	37.5	2.5	5.9	69	417
YOLOv8-VanillaNet	68.0	36.9	2.0	5.9	65	476
DAMO-YOLO-T	79.4	44.7	8.2	17.2	—	182
Gold-YOLOn	69.2	36.9	5.6	12.1	68	370
Tile-YOLO	82.7	46.8	2.8	8.5	78	270

Lightweight large-format tile defect detection algorithm based on improved YOLOv8

基于改进YOLOv8的轻量级大幅面瓷砖缺陷检测算法

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Figures/Tables 14

References 29

Related Articles 15

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Metrics

缺陷类别	YOLOv8n		Tile-YOLO
缺陷类别	mAP@0.5	mAP@0.5：0.95	mAP@0.5	mAP@0.5：0.95
所有	74.5	42.7	82.7	46.8
边异常	91.9	59.5	88.0	54.5
角异常	94.5	64.0	94.7	63.7
白色点	53.7	21.2	75.7	30.4
浅色块	56.7	27.7	68.2	35.1
深色点	66.9	29.7	82.6	38.1
光圈	83.2	54.2	87.1	59.4

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