改进YOLOv4的表面缺陷检测算法

李彬; 汪诚; 丁相玉; 巨海娟; 郭振平; 李卓越

doi:10.13700/j.bh.1001-5965.2021.0301

改进YOLOv4的表面缺陷检测算法

doi: 10.13700/j.bh.1001-5965.2021.0301

空军工程大学基础部，西安 710038

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E-mail：warrant_74@126.com

中图分类号: V239；TP181
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出版历程
- 收稿日期: 2021-06-04
- 录用日期: 2021-10-11
- 网络出版日期: 2023-03-10
- 整期出版日期: 2023-03-30

Surface defect detection algorithm based on improved YOLOv4

Fundamentals Department，Air Force Engineering University，Xi’an 710038，China

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Corresponding author: E-mail：warrant_74@126.com

摘要

摘要:
为解决航空发动机部件表面缺陷检测精度低、检测速度慢的问题,提出一种改进的YOLOv4算法进行智能检测。在路径聚合网络（PANet）结构中融合浅层特征与深层特征，增大特征检测尺度，同时去除自下而上的路径增强结构，提高小目标检测精度和整体检测速度；根据各类缺陷数量不同的情况，优化聚焦损失中的平衡参数，增加权重因子调节各类缺陷的损失权重，将改进后的聚焦损失代替分类误差中的交叉熵损失函数，降低样本不平衡和难易样本对检测精度的影响。实验表明：相比于原始YOLOv4算法，改进后的YOLOv4算法在测试集上的平均精度均值（mAP）为90.10%，提高了2.17%；检测速度为24.82 fps，提高了1.58 fps，检测精度也高于单发多框检测（SSD）算法、EfficientDet算法、YOLOv3算法和YOLOv4-Tiny算法。
- YOLOv4 /
- 表面缺陷检测 /
- 航空发动机 /
- 小目标检测 /
- 聚焦损失
Abstract:
In order to enhance the accuracy and speed of surface defect detection of aeroengine components, an improved YOLOv4 algorithm is proposed for intelligent detection. Firstly, shallow features and deep features were integrated into the path aggregation network (PANet) to improve the feature detection scale, and the bottom-up path augmentation structure was removed to increase the accuracy of small target detection and the overall detection speed. Then, according to the numbers of various defects, the balance parameter of the focal loss was optimized, and a weight factor was added to adjust the loss weight of various defects. The improved focal loss was used to replace the cross-entropy loss function in the classification error, thus reducing the impact of imbalanced samples and hard and easy samples on the detection accuracy. The experimental results show that the mean average precision (mAP) of the improved YOLOv4 on the test set is 90.10%, which is 2.17% higher than that of the traditional YOLOv4, and the detection speed is 24.82 fps, which is increased by 1.58 fps. The detection accuracy is also higher than other algorithms including single shot multibox detector (SSD), EfficientDet, YOLOv3 and YOLOv4-Tiny.
- YOLOv4 /
- surface defect detection /
- aeroengine /
- small target detection /
- focal loss

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图 1 YOLOv4网络结构

Figure 1. YOLOv4 network structure

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图 2 改进的YOLOv4结构

Figure 2. Improved YOLOv4 structure

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图 3 缺陷类型

Figure 3. Defect types

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图 4 聚类中心为12的聚类结果

Figure 4. Clustering results when cluster center is 12

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图 5 不同模型的F_β对比

Figure 5. Comparison of F_βof different models

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图 6 各类缺陷样本的检测结果

Figure 6. Test results of various defect samples

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表 1 数据集中的目标数量

Table 1. Number of targets in dataset

缺陷类型	目标数量			总和
缺陷类型	训练集	验证集	测试集	总和
裂纹	1 579	580	595	2 754
缺口	848	272	270	1 390
凹坑	931	313	349	1 593
划痕	1 782	628	649	3 059

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表 2 不同参数组合下的检测精度对比

Table 2. Comparison of detection accuracy under different parameter combinations

α₀	β₀	mAP/%	AP/%
α₀	β₀	mAP/%	裂纹	缺口	凹坑	划痕
0.20	2.50	88.02	85.04	86.91	91.73	88.38
0.20	3.00	88.24	85.94	87.74	90.66	88.60
0.20	3.50	89.01	84.90	90.62	92.18	88.32
0.25	2.50	89.09	86.40	90.92	90.07	88.95
0.25	3.00	90.10	87.09	91.21	92.65	89.45
0.25	3.50	88.49	84.55	90.09	90.27	89.06
0.30	2.50	89.00	86.62	87.66	92.29	89.41
0.30	3.00	88.62	86.86	90.65	89.30	87.68
0.30	3.50	88.34	85.98	89.53	89.91	87.94

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表 3 不同模型的检测性能对比

Table 3. Comparison of detection performance of different models

网络结构	mAP/%	Speed /fps	AP/%
网络结构	mAP/%	Speed /fps	裂纹	缺口	凹坑	划痕
YOLOv4	87.93	23.24	86.64	92.29	86.02	86.78
YOLOv4-A	89.61	24.94	86.36	93.12	89.10	89.86
YOLOv4-B	90.10	24.82	87.09	91.21	92.65	89.45
注：fps表示帧/s。

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表 4 不同模型的网络结构参数量对比

Table 4. Comparison of network structure parameters of different models

网络结构	总参数量	参数容量/MB
YOLOv4	63 953 841	243.96
YOLOv4-A	48 971 468	186.81
YOLOv4-B	48 971 468	186.81

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表 5 不同算法的检测性能对比

Table 5. Comparison of detection performance of different algorithms

网络结构	mAP/%	Speed/fps
SSD	60.07	33.07
EfficientDet-D0	49.39	13.07
EfficientDet-D1	57.71	10.52
EfficientDet-D2	63.40	9.45
YOLOv3	86.25	30.37
YOLOv4-Tiny	54.25	64.55
YOLOv4	87.93	23.24
YOLOv4-B	90.10	24.82

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