结合YOLOv11改进的双视角X光图像违禁品检测

吴海滨; 刘文柏; 袁鹏飞; 王爱丽

doi:10.3724/CO.2026-0062

结合YOLOv11改进的双视角X光图像违禁品检测

doi: 10.3724/CO.2026-0062

cstr: 32171.14.CO.2026-0062

哈尔滨理工大学黑龙江省激光光谱技术及应用重点实验室, 黑龙江哈尔滨 150080

基金项目: 黑龙江省自然科学基金项目（No. LH2023F034）

详细信息

作者简介:
吴海滨(1977—)，男，上海人，博士，教授，博士生导师。2000年于哈尔滨工业大学获得学士学位，2002年于哈尔滨工业大学获得硕士学位，2008年于哈尔滨理工大学获得博士学位，现为哈尔滨理工大学教授，主要研究方向为计算机视觉、虚拟现实、遥感图像处理。E-mail: woo@hrbust.edu.cn

王爱丽(1979—)，女，天津人，博士，教授，硕士生导师。2002年于哈尔滨工业大学获得学士学位，2004年于哈尔滨工业大学获得硕士学位，2008年于哈尔滨工业大学获得博士学位，现为哈尔滨理工大学教授，主要研究方向为遥感图像处理。E-mail: aili925@hrbust.edu.cn

中图分类号: TP391.4
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出版历程
- 网络出版日期: 2026-07-04

Improved prohibited item detection in double-view X-ray images combined with YOLOv11

Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application, Harbin University of Science and Technology, Harbin 150080, China

Funds: Supported by Natural Science Foundation of Heilongjiang Province of China (No. LH2023F034)

More Information

Corresponding author: aili925@hrbust.edu.cn

摘要

摘要:
针对现有双视角X光安检图像违禁品检测方法在跨视角特征融合过程中自适应性不足、互补信息利用不充分的问题，本文提出一种结合YOLOv11改进的双视角融合检测方法（Dual View Fusion combined with YOLOv11，DVF-YOLOv11）。该算法采用参数共享的双分支YOLOv11骨干网络分别提取俯视图与侧视图的多尺度特征；设计跨视角注意力融合模块（Cross-View Attention Fusion，CVAF），通过通道注意力与空间注意力的级联机制实现双视角特征的自适应增强；采用自适应权重预测网络动态调整各视角融合权重，结合通道压缩卷积形成双路融合策略；设计由特征保留损失、互补性损失和权重平衡损失组成的联合损失函数引导融合学习。在DvXray数据集上，本文方法的mAP50达到94.02%，mAP50-95达到79.41%，较俯视图单视角分别提升2.99%和5.29%。实验结果表明本文方法能够提升双视角X光安检图像中违禁品检测的精度与鲁棒性。
- X光安检 /
- YOLOv11 /
- 双视角融合 /
- 自适应权重融合
Abstract:
To address the issues of insufficient adaptability and inadequate utilization of complementary information in the cross-view feature fusion process of existing prohibited item detection methods for dual-view X-ray security inspection image, we propose an improved dual-view fusion detection method combined with YOLOv11 (Dual View Fusion combined with YOLOv11, DVF-YOLOv11). The proposed method employs a parameter-shared dual-branch YOLOv11 backbone network to extract multi-scale features from the overlook-view and side-view images, respectively. A Cross-View Attention Fusion (CVAF) module is designed to adaptively enhance dual-view features through a cascaded mechanism of channel attention and spatial attention. An adaptive weight prediction network is introduced to dynamically adjust the fusion weights of each view, and is combined with channel compression convolution to form a dual-path fusion strategy. A joint loss function composed of feature preservation loss, complementarity loss, and weight balance loss is further designed to guide the fusion learning process. On the DvXray dataset, the proposed method achieves an mAP50 of 94.02% and an mAP50-95 of 79.41%, improving by 2.99% and 5.29%, respectively, over the single overlook-view baseline. Experimental results demonstrate that the proposed method improves the accuracy and robustness of prohibited item detection in dual-view X-ray security inspection images.
- X-ray security inspection /
- YOLOv11 /
- dual-view fusion /
- adaptive weight fusion

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图 1 DVF-YOLOv11网络结构图

Figure 1. Overall architecture of DVF-YOLOv11

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图 2 CVAF模块结构图

Figure 2. Structural diagram of the CVAF module

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图 3 通道注意力机制结构图

Figure 3. Structural diagram of the channel attention mechanism

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图 4 空间注意力机制结构图

Figure 4. Structural diagram of the spatial attention mechanism

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图 5 DvXray中的X光图像示例

Figure 5. X-ray images in DvXray

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图 6 检测结果的混淆矩阵

Figure 6. Confusion matrix of detection results

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图 7 消融实验训练过程可视化结果

Figure 7. Visualization results of ablation experiment training process

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图 8 双视角形态信息获取示例。（a），（c）为OL视角检测结果；（b），（d）为SD视角检测结果

Figure 8. Examples of dual-view morphological information acquisition. (a), (c) OL perspective detection result; (b), (d) SD perspective detection result

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图 9 形态判别能力检测示例。（a），（c）为OL视角检测结果；（b），（d）为SD视角检测结果

Figure 9. Examples of morphological discrimination detection. (a), (c) OL perspective detection result; (b), (d) SD perspective detection result

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图 10 视角检测盲区互补示例。（a），（c）为OL视角检测结果；（b），（d）为SD视角检测结果

Figure 10. Examples of complementary blind zones between two views. (a), (c) OL perspective detection result; (b), (d) SD perspective detection result

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表 1 各损失项的作用

Table 1. The role of each loss item

损失项	符号	作用	权重
检测损失	$ {\mathcal{L}}_{\text{det}} $	监督目标检测任务	1.0
特征保留损失	$ {\mathcal{L}}_{\text{preserve}} $	保持融合特征与原始特征的一致性	$ {\lambda }_{1} $
互补性损失	$ {\mathcal{L}}_{\text{comp}} $	促进双视角特征的差异性与互补性	$ {\lambda }_{2} $
权重平衡损失	$ {\mathcal{L}}_{\text{balance}} $	防止权重分配的极端化	$ {\lambda }_{3} $

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表 2 不同模型检测性能对比

Table 2. Comparison of detection performance under different models

模型	视角	P(%)	R(%)	F₁(%)	mAP50(%)	mAP50-95(%)	Params(M)	GFLOPs	FPS
YOLOv8	OL	92.03	83.52	87.56	89.63	72.48	3.01	8.1	98.3
	SD	84.31	74.21	78.93	79.28	56.83	3.01	8.1	98.9
	Dual	94.27	86.58	90.27	92.41	77.03	4.75	17.0	45.2
YOLOv10	OL	91.42	82.76	86.87	88.91	71.58	2.27	6.5	75.8
	SD	83.68	73.47	78.26	78.62	55.93	2.27	6.5	76.3
	Dual	93.71	85.86	89.61	91.92	76.04	4.01	13.8	35.6
YOLOv12	OL	92.82	84.23	88.32	90.53	73.51	2.51	5.8	73.8
	SD	85.14	74.92	79.71	80.02	57.86	2.51	5.8	74.5
	Dual	95.08	87.42	91.07	93.21	78.23	4.25	12.4	35.0
YOLOv13	OL	90.73	82.13	86.22	88.17	70.68	2.45	6.2	77.1
	SD	83.04	72.71	77.56	77.93	55.12	2.45	6.2	77.8
	Dual	93.02	85.17	88.91	91.23	75.14	4.19	13.2	36.5
YOLOv11	OL	93.28	84.51	88.69	91.03	74.12	2.58	6.3	107.3
	SD	85.47	75.62	80.27	80.81	59.07	2.58	6.3	108.1
	Dual	95.91	88.32	91.93	94.02	79.41	4.32	13.4	49.6

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表 3 不同融合方法的性能对比

Table 3. Performance comparison of different fusion methods

方法	精确率 (%)	召回率 (%)	F1分数 (%)	mAP50 (%)	mAP50-95 (%)
特征拼接	94.13	85.27	89.48	92.61	76.38
特征相加	94.42	84.53	89.21	92.14	75.23
SE-Net融合^[21]	95.08	86.57	90.68	93.42	77.61
CBAM融合^[22]	95.31	86.24	90.54	93.47	78.18
ECA-Net融合^[23]	94.93	87.38	91.02	93.83	78.71
本文方法	95.91	88.32	91.93	94.02	79.41

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表 4 消融实验结果

Table 4. Results of the ablation experiments

基线模型	A	B	C	D	精确率(%)	召回率(%)	mAP50 (%)	mAP50 −95(%)
√					93.51	84.82	91.27	74.53
√	√				94.03	85.38	91.82	75.47
√		√			93.76	85.63	91.57	75.68
√			√		93.68	85.14	91.46	75.03
√				√	93.57	84.93	91.38	74.72
√	√	√			94.52	86.41	92.53	76.87
√	√		√		94.18	85.82	92.14	76.12
√	√			√	94.07	85.57	91.93	75.83
√		√	√		94.03	85.96	92.04	76.28
√		√		√	93.91	85.78	91.82	76.01
√			√	√	93.82	85.31	91.63	75.27
√	√	√	√		95.37	87.72	93.58	78.63
√	√	√		√	95.21	87.48	93.42	78.27
√	√		√	√	94.68	86.53	92.71	77.14
√		√	√	√	94.47	86.58	92.63	77.23
√	√	√	√	√	95.91	88.32	94.02	79.41

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参考文献(23)

[1]	林俊豪, 张云飞, 陈少伟, 等. 无监督掩码循环对抗网络实现细胞虚拟染色[J]. 中国光学(中英文), 2026, 19(4): 787-800. LIN J H, ZHANG Y F, CHEN SH W, et al. Unsupervised masked cycle-adversarial network for cellular virtual staining[J]. Chinese Optics, 2026, 19(4): 787-800. (in Chinese).
[2]	汪建民, 赵浩冰, 王轲, 等. 无人机飞行单光子动态成像中姿态补偿及重建方法[J]. 中国光学(中英文), 2026, 19(3): 605-618. doi: 10.37188/CO.2026-0004 WANG J M, ZHAO H B, WANG K, et al. Attitude compensation and reconstruction methods for single-photon dynamic imaging during UAV flight[J]. Chinese Optics, 2026, 19(3): 605-618. doi: 10.37188/CO.2026-0004
[3]	XU Y, ZHANG Q Y, SU Q, et al. PIXDet: prohibited item detection in X-ray image based on whole-process feature fusion and local-global semantic dependency interaction[J]. IEEE Transactions on Instrumentation and Measurement, 2023, 72: 5032917. doi: 10.1109/tim.2023.3330184
[4]	WEI Y L, TAO R SH, WU ZH J, et al. Occluded prohibited items detection: an X-ray security inspection benchmark and de-occlusion attention module[C]. Proceedings of the 28th ACM International Conference on Multimedia, ACM, 2020: 138-146.
[5]	TAO R SH, WEI Y L, JIANG X J, et al. Towards real-world X-ray security inspection: a high-quality benchmark and lateral inhibition module for prohibited items detection[C]. 2021 IEEE/CVF International Conference on Computer Vision, IEEE, 2021: 10923-10932.
[6]	ZHU Z M, ZHU Y, WANG H R, et al. FDTNet: enhancing frequency-aware representation for prohibited object detection from X-ray images via dual-stream transformers[J]. Engineering Applications of Artificial Intelligence, 2024, 133: 108076. doi: 10.1016/j.engappai.2024.108076
[7]	刘建军, 冯沛, 廖威, 等. YOLO-STM: 基于Swin-Transformer与MSDA的X光安检图像危险品识别网络[J]. 中国体视学与图像分析, 2024, 29(3): 230-241. doi: 10.13505/j.1007-1482.2024.29.03.008 LIU J J, FENG P, LIAO W, et al. YOLO-STM: a network model for identifying prohibited items in X-ray security inspection images based on Swin-Transformer and MSDA[J]. Chinese Journal of Stereology and Image Analysis, 2024, 29(3): 230-241. (in Chinese). doi: 10.13505/j.1007-1482.2024.29.03.008
[8]	REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection[C]. Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2016: 779-788.
[9]	KHANAM R, HUSSAIN M. YOLOv11: an overview of the key architectural enhancements[J]. arXiv preprint arXiv: 2410.17725, 2024.
[10]	STEITZ J M O, SAEEDAN F, ROTH S. Multi-view X-ray R-CNN[C]. Proceedings of the 40th German Conference on Pattern Recognition, Springer, 2019: 153-168.
[11]	TULI A, BOHRA R, MOGHE T, et al. Automatic threat detection in single, stereo (two) and multi view X-ray images[C]. Proceedings of 2020 IEEE 17th India Council International Conference, IEEE, 2020: 1-7.
[12]	WU M D, YI F F, ZHANG H G, et al. Dualray: dual-view X-ray security inspection benchmark and fusion detection framework[C]. Proceedings of the 5th Chinese Conference on Pattern Recognition and Computer Vision, Springer, 2022: 721-734.
[13]	MENG X L, FENG H, REN Y, et al. Transformer-based dual-view X-ray security inspection image analysis[J]. Engineering Applications of Artificial Intelligence, 2024, 138: 109382. doi: 10.1016/j.engappai.2024.109382
[14]	HONG S L, ZHOU Y Z, XU W C. DAGNet: a dual-view attention-guided network for efficient X-ray security inspection[C]. Proceedings of 2025 International Joint Conference on Neural Networks, IEEE, 2025: 1-8.
[15]	TAO R SH, WANG H Y, GUO Y ZH, et al. Dual-view X-ray detection: can AI detect prohibited items from dual-view X-ray images like humans?[C]. 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2025: 10338-10347.
[16]	MA B W, JIA T, LI M Y, et al. Toward dual-view X-ray baggage inspection: a large-scale benchmark and adaptive hierarchical cross refinement for prohibited item discovery[J]. IEEE Transactions on Information Forensics and Security, 2024, 19: 3866-3878. doi: 10.1109/TIFS.2024.3372797
[17]	VARGHESE R, SAMBATH M. YOLOv8: a novel object detection algorithm with enhanced performance and robustness[C]. Proceedings of the 2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems, IEEE, 2024: 1-6.
[18]	WANG A, CHEN H, LIU L H, et al. YOLOv10: real-time end-to-end object detection[C]. Proceedings of the 38th International Conference on Neural Information Processing Systems, Curran Associates Inc. , 2024: 3429.
[19]	TIAN Y J, YE Q X, DOERMANN D. YOLOv12: attention-centric real-time object detectors[J]. arXiv preprint arXiv: 2502.12524, 2025.
[20]	LEI M Q, LI S Q, WU Y H, et al. YOLOv13: real-time object detection with hypergraph-enhanced adaptive visual perception[J]. arXiv preprint arXiv: 2506.17733, 2025.
[21]	HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C]. Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2018: 7132-7141.
[22]	WOO S, PARK J, LEE J Y, et al. CBAM: convolutional block attention module[C]. Proceedings of the 15th European Conference on Computer Vision, Springer, 2018: 3-19.
[23]	WANG Q L, WU B G, ZHU P F, et al. ECA-Net: efficient channel attention for deep convolutional neural networks[C]. Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2020: 11531-11539.