基于改进SSD算法的紫外像增强器视场瑕疵检测方法

doi:10.12382/bgxb.2023.1091

摘要/Abstract

摘要：

紫外像增强器视场瑕疵是影响器件成像效果的重要因素之一。针对视场瑕疵样本数量少和视场图像显示差异大的问题,采取相应的数据增强策略,并在单发多框检测(Single Shot multibox Detector,SSD)算法的基础上,添加特征金字塔网络(Feature Pyramid Network,FPN),以解决多尺度特征难以有效识别与融合的问题。同时引入卷积注意力模块(Convolutional Block Attention Module,CBAM)去进一步加强网络对小瑕疵目标信息的关注,并抑制噪声干扰。试验结果表明:在自建的数据集上,添加了FPN和CBAM的SSD(Feature Pyramid Network-Convolutional Block Attention Module-Single Shot Multibox Detector,FPN-CBAM-SSD)算法在视场瑕疵实际检测效果方面更优于SSD算法。对于亮点、暗斑、条纹状、亮斑和暗点这5类瑕疵,其平均精准度分别提高了19.76%、22.84%、29.56%、34.55%和38.14%。FPN-CBAM-SSD算法能够满足实际应用需求,适应更加复杂的视场情况,可视为一种有效的紫外像增强器视场瑕疵检测新型方法。

关键词: 紫外像增强器, 视场瑕疵检测, 机器视觉, 深度学习

Abstract:

The field-of-view defect of UV image intensifier is acknowledged as a crucial element impacting the imaging performance of such device. The data enhancement procedures are used to address the issues of few field-of-view defect samples and the significant variances of images in field-of-view. A feature pyramid network (FPN) is added to the single shot multibox detector (SSD) algorithm for the successful detection and fusion of multiscale features. A convolutional block attention module (CBAM) is also introduced to improve the network’s focus on small defect targets and minimize noise interference. The experimental results show that, on the self-constructed dataset, the feature pyramid network-convolutional block attention module-single shot multibox detector (FPN-CBAM-SSD) algorithm outperforms the SSD algorithm significantly in the actual detection of field-of-view defects. For 5 categories of defects including bright spots, dark patches, striped defects, bright patches, and dark spots, the average detection accuracy is improved by 19.76%, 22.84%, 29.56%, 34.55%, and 38.14%, respectively. FPN-CBAM-SSD algorithm is capable of meeting the practical application requirements and adapting to more complex field-of-view conditions, making it an effective method for detecting field-of-view defects in ultraviolet image intensifiers.

Key words: ultraviolet image intensifier, field-of-view defect detection, machine vision, deep learning

中图分类号:

TN23，TP391.4

丁习文, 程宏昌, 袁渊, 苏悦. 基于改进SSD算法的紫外像增强器视场瑕疵检测方法[J]. 兵工学报, 2024, 45(12): 4350-4363.

DING Xiwen, CHENG Hongchang, YUAN Yuan, SU Yue. Detection Method for Field-of-view Defect of Ultraviolet Image Intensifier Based on Improved SSD Algorithm[J]. Acta Armamentarii, 2024, 45(12): 4350-4363.

图/表 22

图1 紫外像增强器视场瑕疵检测实验整体流程

Fig.1 Overall flowchart of the UV image intensifier field-of-view defect detection experiment

图2 不同采集批次图像显示效果存在差异

Fig.2 Differences in the display effects of images from different acquisition batches

图3 图像数据增强示例效果

Fig.3 Example effect of image data enhancement

图4 紫外像增强器视场中主要类型瑕疵

Fig.4 Main types of defects in the field of view of UV image intensifier

图5 紫外像增强器视场瑕疵标注

Fig.5 Labeling of field of view defects of UV image intensifier

图6 视场中主要类型瑕疵数量大致占比情况

Fig.6 Approximate proportion of the number of major types of defects in the field of view

图7 SSD检测算法的结构示意图

Fig.7 Structure diagram of SSD detection algorithm

图8 数据集中的标注框尺寸及分布情况

Fig.8 Size and distribution of labeled boxes in the dataset

图9 不同检测框尺寸的检测结果

Fig.9 Detection results for different anchor-sizes

图10 特征金字塔网络示意图

Fig.10 Schematic diagram of feature pyramid network

图11 卷积注意力机制示意图

Fig.11 Schematic diagram of convolutional attention mechanisms

图12 FPN-CBAM-SSD算法结构示意图

Fig.12 Schematic diagram of the structure of FPN-CBAM-SSD algorithm

表1 实验平台硬件环境配置情况

Table 1 Configuration of hardware environment of the experimental platform

实验硬件配置		型号	生产厂家
光源		BM-Ⅰ No.1608	上海灯泡三厂
外接电源		GWLNSTEK GPP-4323	固纬电子(苏州)有限公司
数码相机		Canon DS126151 400D	佳能(中国)有限公司
计算机	处理器	Intel(R) Core(TM) i5-12400 4.40GHz	英特尔(中国)有限公司
	显卡	Nvidia GTX 3060 12GB	技嘉科技股份有限公司

表2 实验平台软件环境配置情况

Table 2 Configuration of software environment of the experimental platform

实验软件环境	具体版本号
Python	3.6
CUDA	11.3
CuDnn	8.4.1

图13 不同优化SSD算法的损失曲线

Fig.13 Loss trends of different improved SSD algorithms

表3 消融试验性能指标对比

Table 3 Comparison of ablation test performance indexes

算法	准确率/ %	召回率/ %	F1分数/ %	mAP/%					mAP/ %	FPS/ (帧·s^-1)
算法	准确率/ %	召回率/ %	F1分数/ %	暗点	亮点	暗斑	亮斑	条纹状	mAP/ %	FPS/ (帧·s^-1)
SSD算法	90.0	14.3	23.2	22.97	43.05	48.57	42.12	27.07	36.76	70.90
CBAM-SSD算法	87.6	26.4	40.5	32.80	46.12	60.58	53.42	36.89	45.96	63.53
FPN-SSD算法	74.7	32.9	43.4	44.82	40.13	66.29	57.56	38.49	49.46	51.47
FPN-CBAM-SSD算法	81.9	51.9	63.2	61.11	62.81	71.41	76.67	56.63	65.73	15.57

表4 不同检测算法在自建紫外像增强器视场数据集上的性能对比

Table 4 Performance comparison of different detection algorithms on the self-built UV imageintensifier field of view dataset

算法	平均精准度/%					mAP/ %	参数量/ 10⁶	GFLOPS/ G	FPS/ (帧·s^-1)
算法	暗点	亮点	暗斑	亮斑	条纹状	mAP/ %	参数量/ 10⁶	GFLOPS/ G	FPS/ (帧·s^-1)
SSD算法	22.97	43.05	48.57	42.12	27.07	36.76	24.15	61.14	70.90
FPN-CBAM-SSD算法	61.11	62.81	71.41	76.67	56.63	65.73	30.83	66.13	15.57
Faster-RCNN算法	18.54	23.53	72.85	61.16	42.84	43.78	136.77	369.82	11.92
YOLOv5算法	31.41	23.40	32.04	32.79	25.46	29.02	7.07	16.51	102.19
YOLOv8算法	31.84	25.25	36.72	34.62	27.79	31.25	11.14	28.66	96.11

表5 不同检测算法在VOC 07+12数据集上的性能对比

Table 5 Performance comparison of different detection a lgorithms on VOC 07+12 dataset

算法	mAP/ %	参数量/ 10⁶	GFLOPS/ 10⁹	FPS/ (帧·s^-1)
SSD算法	79.24	26.15	62.65	46.38
FPN-CBAM-SSD算法	83.63	31.77	66.83	13.96
Faster-RCNN算法	78.46	137.08	370.19	7.19
YOLOv5算法	77.12	7.12	16.64	101.41
YOLOv8算法	81.61	11.14	28.69	93.94

表6 典型情况测试结果

Table 6 Typical case test results

图14 典型情况下视场瑕疵检测结果的数据统计分析

Fig.14 Statistical analysis of data for detected results of field-of-view defects in typical cases

表7 复杂视场情况测试结果

Table 7 Complex field-of-view test results

图15 复杂情况下视场瑕疵检测结果的数据统计分析

Fig.15 Statistical analysis of data for detected results of field-of-view defects in complex cases

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