Research on the Identification of Spherical Explosive Fragmentation Damage Effect Based on Siamese Networks and Regional Attention Mechanisms

doi:10.12382/bgxb.2023.1215

Abstract

Abstract:

In the information warfare, the damage assessment of explosive fragments is of great significance to achieve accurate strike. However, the manual acquisition of distribution and geometric information of damage areas are inefficient in the damage experiment. To this end, a lightweight image segmentation model based on siamese networks and regional attention mechanisms is proposed, which achieves the efficient and accurate recognition of small-targeted spherical explosive fragmentation damage area under small samples. The model’s ability to perceive the explosion holes is improved by introducing the siamese structure, regional attention module and multi-scale convolution module. A loss function with multiple constraints is added and the best optimizer is screened so that the model optimization is more focused on the effective information for accelerating the model convergence. A quantitative detection method for the damaged area based on the connected-domain fusion watershed algorithm is proposed to achieve the accurate identification of the overlapping case of explosion broken holes. Experimental results show that the proposed method achieves higher efficiency and accuracy compared with the current mainstream models, and the average errors in predicting the area and diameter of damage region are 4.78% and 3.79%, respectively. The research work provides a reference for realizing the intelligent damage assessment of explosives containing fragments.

Key words: siamese network, damage effect assessment, damage recognition, regional attention mechanism, multi-scale convolution

CLC Number:

TJ06
TP29

LI Haotian, CUI Xinyu, LIU Mengzhen, HUANG Guangyan, LÜ Zhongjie, ZHANG hong. Research on the Identification of Spherical Explosive Fragmentation Damage Effect Based on Siamese Networks and Regional Attention Mechanisms[J]. Acta Armamentarii, 2024, 45(12): 4259-4271.

Figures/Tables 20

Fig.1 Schematic diagram of the explosive test with fragmentation

Fig.2 Data enhancement methods

Fig.3 Example of damage area matrix labeling

Fig.4 Annotation of damage area

Fig.5 Structureof damage area quantization algorithm based SALU-Net

Fig.6 Siamese classification module

Fig.7 Regional attention mechanism

Fig.8 Regional attention enhanced visualization

Fig.9 Feeling fields fordilated convolution at different dilation rates

Fig.10 Multiscale convolution module based on dilated convolution

Table 1 Network parameters at each stage

阶段	学习率	损失	训练轮次
1	0.02	L_s+L_c	10
2	0.002	L_s+L_c+L_seg	50
3	0.0002	L_seg	10

Table 2 Ablation experimental results of network structure modules

模型	查准率/%	召回率/%	F1值/ %	平均交并比/%	单帧耗时/%
LU-Net	59.1	94.7	72.8	78.3	14.5
LU-Net+MCL	81.7	90.4	85.8	87.5	14.5
LU-Net+MCL+RAM	82.5	94.1	87.9	89.2	15.1
LU-Net+MCL+ RAM+MCM	88.6	95.3	91.7	92.4	17.5

Fig.11 Changes of loss with epoch for four optimizers during training

Table 3 Experimental results of four optimizers

优化器	F1值/%	MIoU/%
SGD	91.785	92.357
Adagrad	91.250	91.900
Adam	94.669	94.907
RMSprop	95.241	95.428

Table 4 Evaluation indexes of the different segmentation models on the datasets in this paper

模型	F1值/%	MIoU/%	单帧耗时/ms
Deeplabv3^[34]	83.553	85.760	40.6
U-Net^[24]	94.296	94.570	47.3
MALUNet^[35]	79.997	83.206	12.6
SALU-Net	95.241	95.428	17.5

Fig.12 Quantitative results of damage area

Fig.13 Error rate distribution of broken hole area and diameter

Fig.14 Histogram of sample statistics of broken hole error rate

Table 5 The damage identification effect of SALU-Net model

Fig.15 Platform for visualization and detection of fragmentation damage areas

References 36

[1]	李峰, 石全, 孙正. 目标毁伤效果评估技术研究综述[J]. 兵器装备工程学报, 2018, 39(9): 69-72.
	LI F, SHI Q, SUN Z. Summary of technical research on the evaluation of target mutilation[J]. Journal of Ordnance Equipment Engineering, 2018, 39(9): 69-72. (in Chinese)
[2]	LI H S, ZHANG X Q, ZHANG X W. Calculation model and method of target damage efficiency assessment based on warhead fragment dispersion[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1-8.
[3]	JENA B, SAXENA S, NAYAK G K, et al. Artificial intelligence-based hybrid deep learning models for image classification: the first narrative review[J]. Computers in Biology and Medicine, 2021, 137: 104803.
[4]	JIAO L C, ZHANG F, LIU F, et al. A survey of deep learning-based object detection[J]. IEEE Access, 2019, 7: 128837-128868. doi: 10.1109/ACCESS.2019.2939201
[5]	THOMA M. A survey of semantic segmentation: arXiv:1602.06541[R/OL]. Ithaca, NY, US: Cornell University, 2016(2016-05-11). https://arxiv.org/abs/1602.06541.
[6]	BRODLEY C E, UTGOFF P E. Multivariate decision trees[J]. Machine Learning, 1995, 19(1): 45-77.
[7]	SHAWE-TAYLOR J, SUN S. A review of optimization methodologies in support vector machines[J]. Neurocomputing, 2011, 74(17): 3609-3618.
[8]	de AMORIM R C. A survey on feature weighting based K-Means algorithms: arXiv:1602.06541[R/OL]. Ithaca, NY, US: Cornell University, 2016(2016-05-11). https://arxiv.org/abs/1602.06541.
[9]	LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324.
[10]	KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84-90.
[11]	GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation: arXiv:1311.2524[R/OL]. Ithaca, NY, US: Cornell University, 2014(2014-10-22). https://arxiv.org/abs/1311.2524.
[12]	WEI F J, YAO G, YANG Y, et al. Instance-level recognition and quantification for concrete surface bughole based on deep learning[J]. Automation in Construction, 2019, 107: 102920.
[13]	雷江波, 王泽民, 李静. 基于Faster R-CNN的破片群图像目标检测研究[J]. 国外电子测量技术, 2021, 40(1): 70-74.
	LEI J B, WANG Z M, LI J. Detection of objects in fragments based on Faster R-CNN[J]. Foreign Electronic Measurement Technology, 2021, 40(1): 70-74. (in Chinese)
[14]	魏琦, 何子清, 王亚林, 等. 基于DenseNet和注意力机制的静爆场破片识别方法研究[J]. 兵器装备工程学报, 2023, 44(2): 259-265.
	WEI Q, HE Z Q, WANG Y L, et al. Research on fragment identification of a static explosion field based on DenseNet and attention mechanism[J]. Journal of Ordnance Equipment Engineering, 2023, 44(2): 259-265. (in Chinese)
[15]	LIU D B, HE Z N, CHEN D D, et al. A network framework for small-sample learning[J]. IEEE Transactions on Neural Networks and Learning Systems, 2020, 31(10): 4049-4062.
[16]	WANG Y Q, YAO Q M, KWOK J, et al. Generalizing from a few examples: a survey on few-shot learning: arXiv:1904.05046[R/OL]. Ithaca, NY, US: Cornell University, 2020(2020-03-29). https://arxiv.org/abs/1904.05046.
[17]	FINN C, ABBEEL P, LEVINE S. Model-agnostic meta-learning for fast adaptation of deep networks: arXiv:1703.03400[R/OL]. Ithaca, NY, US: Cornell University, 2017(2017-07-18). https://arxiv.org/abs/1703.03400.
[18]	YI C C, CHEN Q R, XU B, et al. Steel strip defect sample generation method based on fusible feature GAN model under few samples[J]. Sensors, 2023, 23(6): 3216.
[19]	WANG H H, LI Z L, WANG H Q. Few-shot steel surface defect detection[J]. IEEE Transactions on Instrumentation and Measurement, 2022, 71: 5003912.
[20]	XU H S, ZHU Y C. Real-time object tracking based on improved fully-convolutional siamese network[J]. Computers & Electrical Engineering, 2020, 86: 106755.
[21]	FENG K X, CHASPARI T. Few-shot learning in emotion recognition of spontaneous speech using a siamese neural network with adaptive sample pair formation[J]. IEEE Transactions on Affective Computing, 2023, 14(2): 1627-1633.
[22]	PEI M T, YAN B, HAO H L, et al. Person-specific face spoofing detection based on a siamese network[J]. Pattern Recognition, 2023, 135: 109148.
[23]	LUAN C H, JING Z B, ZUO J H. A defect-sensitive loss function based on siamese network to defect detection with imbalanced samples[J]. Journal of Physics: Conference Series, 2021, 1802(4): 042085.
[24]	ZHAO X P, MA M Y, SHAO F. Bearing fault diagnosis method based on improved Siamese neural network with small sample[J]. Journal of Cloud Computing, 2022, 11(1): 79.
[25]	RONNEBERGER O, FISCHER P, BROX T. U-Net: convolutional networks for biomedical image segmentation[C]//Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015. Munich, Germany:Springer, 2015: 234-241.
[26]	NIU Z Y, ZHONG G Q, YU H. A review on the attention mechanism of deep learning[J]. Neurocomputing, 2021, 452: 48-62.
[27]	YU F, KOLTUN V. Multi-scale context aggregation by dilated convolutions: arXiv:1511.07122[R/OL]. Ithaca, NY, US: arXivCornell University, 2016(2016-04-30). https://arxiv.org/abs/1511.07122.
[28]	惠康华, 杨卫, 刘浩翰, 等. 基于YOLOv5的增强多尺度目标检测方法[J]. 兵工学报, 2023, 44(9): 2600-2610. doi: 10.12382/bgxb.2022.1147
	HUI K H, YANG W, LIU H H, et al. Enhanced multi-scale target detection method based on YOLOv5[J]. Acta Armamentarii, 2023, 44(9): 2600-2610. (in Chinese) doi: 10.12382/bgxb.2022.1147
[29]	SZEGEDY C, IOFFE S, VANHOUCKE V, et al. Inception-v4, inception-resnet and the impact of residual connections on learning: arXiv:1602.07261[R/OL]. Ithaca, NY, US: Cornell University, 2016(2016-08-23). https://arxiv.org/abs/1602.07261.
[30]	ZHOU D Q, HOU Q B, CHEN Y P, et al. Rethinking bottleneck structure for efficient mobile network design[C]//Computer Vision-ECCV 2020. Glasgow, UK:Springer, 2020.
[31]	LI X Y, SUN X F, MENG Y X, et al. Dice loss for data-imbalanced NLP tasks[C]// Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Online: Association for Computational Linguistics, 2020: 465-476.
[32]	ADYA M, COLLOPY F. How effective are neural networks at forecasting and prediction? a review and evaluation[J]. Journal of Forecasting, 1998, 17(5/6): 481-495.
[33]	刘梦真, 黄广炎, 张宏, 等. 小样本驱动特征分段网络的防护材料折痕检测[J]. 兵工学报, 2024, 45(3): 963-974. doi: 10.12382/bgxb.2022.0884
	LIU M Z, HUANG G Y, ZHANG H, et al. Protective material crease detection with small sample-driven feature segmented neural network[J]. Acta Armamentarii, 2024, 45(3): 963-974. (in Chinese) doi: 10.12382/bgxb.2022.0884
[34]	RUAN J C, XIANG S C, XIE M Y, et al. MALUNet: a multi-attention and light-weight unet for skin lesion segmentation: arXiv:2211.01784[R/OL]. Ithaca, NY, US: Cornell University, 2022(2022-11-03). https://arxiv.org/abs/2211.01784.
[35]	CHEN L C, ZHU Y, PAPANDREOU G, et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[C]// Computer Vision-ECCV 2018. Munich, Germany: Springer, 2018: 833-851.
[36]	PENG C, LIU Y K, GUI W H, et al. Bubble Image segmentation based on a novel watershed algorithm with an optimized mark and edge constraint[J]. IEEE Transactions on Instrumentation and Measurement, 2022, 71: 5005110.