基于改进级联R-CNN的面料疵点检测方法

doi:10.19398/j.att.202106018

摘要/Abstract

摘要：

由于布匹疵点种类分布不均,部分疵点具有极端的宽高比,而且小目标较多,导致检测难度大,因此提出一种改进级联R-CNN的布匹疵点检测方法。针对小目标问题,在R-CNN部分采用在线难例挖掘,加强对小目标的训练;针对布匹疵点极端的长宽比,在特征提取网络中采用了可变形卷积v2来代替传统的正方形卷积,并结合布匹特征重新设计边界框比例。最后采用完全交并比损失作为边界框回归损失,获取更精确的目标边界框。结果表明:对比改进前的模型,改进后的模型预测边界框更加精确,对小目标的疵点检测效果更好,在准确率上提升了3.57%,平均精确度均值提升了6.45%,可以更好地满足面料疵点的检测需求。

关键词: 级联R-CNN, 面料疵点, 检测, 可变形卷积v2, 在线难例挖掘, 完全交并比损失

Abstract:

To solve the difficult detection problem due to the uneven distribution of different fabric defects, extreme aspect ratios existing in some defects, and a large number of small targets, a method for fabric defect detection based on improved cascade R-CNN was proposed. The difficult examples were mined online in R-CNN part to strengthen small target training. To address the issue of the extreme aspect ratio of fabric defects, the traditional square volume in the feature extraction network was replaced by deformable convolution v2. The scale of the bounding box was redesigned according to the characteristics of the fabric. Finally, the complete intersection over union loss was adopted as the bounding box regression loss, and a more accurate target bounding box was obtained. The experimental results indicated that the improved model was more accurate in predicting the bounding box than that before improvement, and it achieved a better effect on small target detection. The accuracy was improved by 3.57%, and the average accuracy was improved by 6.45%. Therefore, it can better meet the requirements of fabric defect detection.

Key words: cascade R-CNN, fabric defect, detection, deformable convolution v2, online difficult example mining, complete intersection over union loss

中图分类号:

许胜宝, 郑飂默, 袁德成. 基于改进级联R-CNN的面料疵点检测方法[J]. 现代纺织技术, 2022, 30(2): 48-56.

XU Shengbao, ZHENG Liaomo, YUAN Decheng. A method for fabric defect detection based on improved cascade R-CNN[J]. Advanced Textile Technology, 2022, 30(2): 48-56.

图/表 14

参考文献 23

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编号	瑕疵	图片数/张	目标数/个	编号	瑕疵	图片数/张	目标数/个
1	破洞	150	312	11	吊经	147	160
2	水渍、油渍、污渍	259	401	12	粗纬	545	915
3	三丝	845	1055	13	纬缩	262	452
4	结头	1447	1996	14	浆斑	284	336
5	花板跳	122	134	15	整经结	186	473
6	百脚	140	161	16	星跳、跳花	295	302
7	毛粒	160	195	17	断氨纶	222	546
8	粗经	205	213	18	稀密档、浪纹档、色差档	354	361
9	松经	359	396	19	磨痕、轧痕、修痕、烧毛痕	415	470
10	断经	265	284	20	死皱、云织、双纬、双经、跳纱、筘路、纬纱不良	289	361

编号	瑕疵	图片数/张	目标数/个	编号	瑕疵	图片数/张	目标数/个
1	破洞	150	312	11	吊经	147	160
2	水渍、油渍、污渍	259	401	12	粗纬	545	915
3	三丝	845	1055	13	纬缩	262	452
4	结头	1447	1996	14	浆斑	284	336
5	花板跳	122	134	15	整经结	186	473
6	百脚	140	161	16	星跳、跳花	295	302
7	毛粒	160	195	17	断氨纶	222	546
8	粗经	205	213	18	稀密档、浪纹档、色差档	354	361
9	松经	359	396	19	磨痕、轧痕、修痕、烧毛痕	415	470
10	断经	265	284	20	死皱、云织、双纬、双经、跳纱、筘路、纬纱不良	289	361

疵点编号	算法
疵点编号	Pre(Cascade R-CNN)	Pre+OHEM	Pre+OHEM+DCN v2	Pre+OHEM+DCN v2+CIoU Loss
1	68.4	69.1	71.0	73.4
2	33.9	37.6	36.8	38.9
3	70.6	71.4	73.4	73.6
4	45.4	46.7	50.0	51.4
5	82.9	83.6	86.6	90.9
6	42.6	46.8	53.2	58.6
7	29.3	34.6	38.9	38.3
8	57.7	61.1	65.7	65.7
9	63.9	64.4	69.9	70.9
10	43.1	46.6	51.3	52.0
11	54.6	54.8	55.7	55.6
12	49.9	51.2	51.4	54.9
13	31.8	37.3	39.8	40.8
14	80.2	79.9	82.2	83.2
15	30.2	34.9	32.5	30.3
16	50.4	52.3	58.2	58.4
17	37.6	41.9	44.1	49.6
18	28.6	33.2	29.6	30.6
19	40.9	42.8	44.9	44.9
20	34.2	39.6	40.4	43.2
ACC	93.63	92.87	96.47	97.20
mAP	48.81	51.49	53.78	55.26

疵点编号	算法
疵点编号	Pre(Cascade R-CNN)	Pre+OHEM	Pre+OHEM+DCN v2	Pre+OHEM+DCN v2+CIoU Loss
1	68.4	69.1	71.0	73.4
2	33.9	37.6	36.8	38.9
3	70.6	71.4	73.4	73.6
4	45.4	46.7	50.0	51.4
5	82.9	83.6	86.6	90.9
6	42.6	46.8	53.2	58.6
7	29.3	34.6	38.9	38.3
8	57.7	61.1	65.7	65.7
9	63.9	64.4	69.9	70.9
10	43.1	46.6	51.3	52.0
11	54.6	54.8	55.7	55.6
12	49.9	51.2	51.4	54.9
13	31.8	37.3	39.8	40.8
14	80.2	79.9	82.2	83.2
15	30.2	34.9	32.5	30.3
16	50.4	52.3	58.2	58.4
17	37.6	41.9	44.1	49.6
18	28.6	33.2	29.6	30.6
19	40.9	42.8	44.9	44.9
20	34.2	39.6	40.4	43.2
ACC	93.63	92.87	96.47	97.20
mAP	48.81	51.49	53.78	55.26

光照强度	ACC/%	mAP/%
3.0	96.00	49.83
4.0	96.27	49.35
4.5	96.67	50.84
5.0	96.83	54.87
5.5	97.27	51.78
6.0	97.27	50.95
7.0	96.73	49.66
10.0	94.93	48.05
未设置强度	97.20	55.26