面向可见光与红外多模态目标检测的对抗攻防综述

doi:10.11896/jsjkx.241200151

Abstract

Abstract: Object detection,as a fundamental classic task in the field of computer vision,has a wide range of applications.Deep learning based object detection algorithms have become the mainstream of current research due to their superior performance.However,most object detection algorithms only perform single-mode detection on visible or infrared images.In general,visible images have poor imaging in harsh weather,nighttime,and scenes,where targets are obstructed,leading to a decrease in detection performance.The use of infrared images can improve the above issues,but infrared images may miss some details of the target.Therefore,multimodal fusion detection algorithms based on visible light and infrared images are gradually emerging.However,existing research has focused on improving the performance of multimodal object detection algorithms,and research on their security is relatively scattered.Based on existing research work,this paper provides an overview of the security of multimodal object detection in adversarial situations.Firstly,a theoretical analysis of multimodal object detection and attack and defense is conducted.Secondly,multimodal object detection methods are classified and summarized according to fusion detection in different time periods.Then,existing methods of object detection and adversarial defense are summarized and organized,and the existing dataset and main evaluation indicators of multimodal object detection are summarized.Finally,potential research directions for multi-modal object detection in the future are discussed,further promoting the development and application of multimodal object detection in adversarial security research.

Key words: Object detection, Deep learning, Multimodal object detection, Adversarial attacks, Defense

CLC Number:

TP391.4

ZHENG Haibin, LIN Xiuhao, CHEN Jingwen, CHEN Jinyin. Survey of Adversarial Attack and Defense for RBG and Infrared Multimodal Object Detection[J].Computer Science, 2025, 52(11): 349-363.

References

[1]REN S,HE K,GIRSHICK R,et al.Faster R-CNN:Towardsreal-time object detection with region proposal networks [C]//Advances in Neural Information Processing Systems.2015.
[2]MA Y,WU Z Y,JIANG X.Object Detection based on Feature Fusion of Infrared and Visible Images [J].Missiles and Space Vehicles,2022,389(5):83-87.
[3]KIEU M,BAGDANOV A D,BERTINI M,et al.Task-conditioned domain adaptation for pedestrian detection in thermal ima-gery [C]//European Conference on Computer Vision.Cham:Springer,2020:546-562.
[4]DEVAGUPTAPU C,AKOLEKAR N,SHARMA M,et al.Borrow from anywhere:Pseudo multi-modal object detection in thermal imagery [C]//Proceedings of the IEEE/CVFConfe-rence on Computer Vision and Pattern Recognition Workshops.2019.
[5]ZHANG L,ZHU X,CHEN X,et al.Weakly aligned cross-modal learning for multispectral pedestrian detection [C]//Procee-dings of the IEEE/CVF International Conference on Computer Vision.2019:5127-5137.
[6]PANCHOTIYA B,ISRANI D,PATEL R.An efficient image fusion of visible and infrared band images using integration of anisotropic diffusion and discrete wavelet transform [J].Journal of Communications Software and Systems,2020,16(1):30-36.
[7]CAO Y Q,YANG S C.Image fusion method based on convolutional sparse representation [J].Navigation and Control,2020,19(2):97-105.
[8]LIN D,PAN L,YI P.Research progress on robustness of convolutional neural networks for image recognition [J].Chinese Journal of Network and Information Security,2022,8(3):111-122.
[9]TANG L,YUAN J,ZHANG H,et al.PIAFusion:A progressive infrared and visible image fusion network based on illumination aware [J].Information Fusion,2022,83:79-92.
[10]LI H,WU X,KITTLER J.RFN-Nest:An end-to-end residualfusion network for infrared and visible images [J].Information Fusion,2021,73:72-86.
[11]LI C,SONG D,TONG R,et al.Multispectral pedestrian detection via simultaneous detection and segmentation [J].arXiv:1808.04818,2018.
[12]LIU X,YANG H,LIU Z,et al.Dpatch:An adversarial patch attack on object detectors [C]//Proceedings of the IEEE Confe-rence on Computer Vision and Pattern Recognition.2020:2849-2858.
[13]XU K,ZHANG G,LIU S,et al.Adversarial T-shirt! Evading person detectors in a physical world [C]//European Conference on Computer Vision.Cham:Springer,2020:665-681.
[14]ZHU X,HU Z,HUANG S,et al.Infrared invisible clothing:Hiding from infrared detectors at multiple angles in real world [C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2022:13317-13326.
[15]LI Y,TIAN D,CHANG M C,et al.Robust adversarial perturbation on deep proposal-based models [C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2019:8082-8091.
[16]ZHANG H,ZHOU W,LI H.Contextual adversarial attacks for object detection [C]//IEEE International Conference on Multimedia and Expo.2020:1-6.
[17]LIAO Q,WANG X,KONG B,et al.Category-wise attack:Transferable adversarial examples for anchor free object detection [C]//IEEE International Conference on Multimedia and Expo.2021:1-6.
[18]WEI X,YU J,HUANG Y.Physically adversarial infrared patches with learnable shapes and locations [C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2023:12334-12342.
[19]WEI H,WANG Z,JIA X,et al.Hotcold block:Fooling thermal infrared detectors with a novel wearable design [C]//Procee-dings of the AAAI Conference on Artificial Intelligence.2023:15233-15241.
[20]HU C,SHI W,JIANG T,et al.Adversarial infrared blocks:A multi-view black-box attack to thermal infrared detectors in physical world [J].Neural Networks,2024,175:106310.
[21]KIM T,LEE H J,RO Y M.MAP:Multispectral adversarialpatch to attack person detection [C]//ICASSP 2022－2022 IEEE International Conference on Acoustics,Speech and Signal Processing(ICASSP).IEEE,2022:4853-4857.
[22]WEI X,HUANG Y,SUN Y,et al.Unified adversarial patch for cross-modal attacks in the physical world [C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.2023:4445-4454.
[23]KIM T,YU Y,RO Y M.Multispectral invisible coating:Laminated visible-thermal physical attack against multispectral object detectors using transparent Low-e films [C]//Proceedings of the AAAI Conference on Artificial Intelligence.2023:1151-1159.
[24]SZEGEDY C,ZAREMBA W,SUTSKEVER I,et al.Intriguing properties of neural networks [J].arXiv:1312.6199,2013.
[25]GOODFELLOW I J,SHLENS J,SZEGEDY C.Explaining and harnessing adversarial examples [J].arXiv:1412.6572,2014.
[26]KURAKIN A,GOODFELLOW I,BENGIO S.Adversarial examples in the physical world [J].arXiv:1607.02533,2016.
[27]MADRY A,MAKELOV A,SCHMIDT L,et al.Towards deep learning models resistant to adversarial attacks [J].arXiv:1706.06083,2017.
[28]XIE C,WANG J,ZHANG Z,et al.Adversarial examples for semantic segmentation and object detection [C]//Proceedings of the IEEE International Conference on Computer Vision.2017:1369-1378.
[29]YU Y,LEE H J,KIM B C,et al.Investigating vulnerability to adversarial examples on multimodal data fusion in deep learning [J].arXiv:2005.10987,2020.
[30]SHEN Y,XIANG K,CHEN X,et al.A noisy infrared and visible light image fusion algorithm [J].The Journal of Information Processing Systems,2021,17(5):1004-1019.
[31]HU D,SHI H.Infrared and visible image fusion based on empirical curvelet transform and phase congruency [J].Ukrainian Journal of Physics,2021,22:128-137.
[32]XING X,LIU C,LUO C,et al.Infrared and visible image fusion based on nonlinear enhancement and NSST decomposition [J].EURASIP Journal on Wireless Communications and Networking,2020,2020(1):162.
[33]ZHU Y,LU Y,GAO Q,et al.Infrared and visible image fusion based on convolutional sparse representation and guided filtering [J].Journal of Electronic Imaging,2021,30(4):043003.
[34]LIU F,CHEN L,LU L,et al.Infrared and visible image fusion via rolling guidance filter and convolutional sparse representation [J].Journal of Intelligent and Fuzzy Systems,2021,40(6):10603-10616.
[35]LIU G,YAN S.Latent low-rank representation for subspacesegmentation and feature extraction [C]//IEEE International Conference on Computer Vision.Barcelona:IEEE Computer Society,2011:1615-1622.
[36]LI H,WU X,KITTLER J.MDLatLRR:A novel decomposition method for infrared and visible image fusion [J].IEEE Transactions on Image Processing,2020,29:4733-4746.
[37]SUN B,ZHUGE W W,GAO Y X,et al.Infrared and Visible Image Fusion Based on Latent Low-Rank Representation [J].Infrared Technology,2022,44(8):853-862.
[38]YANG Y,ZHANG Y,HUANG S,et al.Infrared and visible image fusion using visual saliency sparse representation and detail injection model [J].IEEE Transactions on Instrumentation and Measurement,2021,70:1-15.
[39]MA J,MA Y,LI C.Infrared and visible image fusion methods and applications:a survey [J].Information Fusion,2019,45:153-178.
[40]LI H.Research and Application of Image Fusion AlgorithmsBased on Representation Learning [D].Wuxi:Jiangnan University,2021.
[41]BAVIRISETTI D,DHULI R.Two-scale image fusion of visible and infrared images using saliency detection [J].Infrared Phy-sics & Technology,2016,76:52-64.
[42]MA J,TANG L,XU M,et al.STDFusionNet:An infrared and visible image fusion network based on salient target detection [J].IEEE Transactions on Instrumentation and Measurement,2021,70:1-13.
[43]LI H,WU X.DenseFuse:A fusion approach to infrared and visible images [J].IEEE Transactions on Image Processing,2019,28(5):2614-2623.
[44]LI H,WU X,DURRANI T.NestFuse:An infrared and visible image fusion architecture based on nest connection and spatial/channel attention models [J].IEEE Transactions on Instrumentation and Measurement,2020,69(12):9645-9656.
[45]GOODFELLOW I,POUGET-ABADIE J,MIRZA M,et al.Ge-nerative adversarial networks [J].Communications of the ACM,2020,63(11):139-144.
[46]WANG Z L,ZHANG B W.Review of Generative AdversarialNetworks [J].Chinese Journal of Network and Information Security,2021,7(4):68-85.
[47]MA J,YU W,LIANG P,et al.FusionGAN:A generative adversarial network for infrared and visible image fusion [J].Information Fusion,2019,48:11-26.
[48]MA J,XU H,JIANG J,et al.DDcGAN:A dual-discriminator conditional generative adversarial network for multi-resolution image fusion [J].IEEE Transactions on Image Processing,2020,29:4980-4995.
[49]MA J,ZHANG H,SHAO Z,et al.GANMcC:A generative adversarial network with multiclassification constraints for infrared and visible image fusion [J].IEEE Transactions on Instrumentation and Measurement,2021,70:1-14.
[50]GUAN D,CAO Y,YANG J,et al.Fusion of multispectral data through illumination-aware deep neural networks for pedestrian detection [J].Information Fusion,2019,50:148-157.
[51]ZHANG L,ZHU X,CHEN X,et al.Weakly aligned cross-modal learning for multispectral pedestrian detection [C]//Procee-dings of the IEEE/CVF International Conference on Computer Vision.2019:5127-5137.
[52]CHEN Y T,SHI J,YE Z,et al.Multimodal object detection via probabilistic ensembling [C]//European Conference on Computer Vision.Cham:Springer,2022:139-158.
[53]KRIZHEVSKY A,SUTSKEVER I,HINTON G E.Imagenetclassification with deep convolutional neural networks [C]//Advances in Neural Information Processing Systems.2012:1097-1105.
[54]DOLLAR P,WOJEK C,SCHIELE B,et al.Pedestrian detec-tion:A benchmark [C]//CVPR.2009.
[55]XU P,DAVOINE F,DENOEUX T.Evidential combination of pedestrian detectors [C]//British Machine Vision Conference.2014:1-14.
[56]PEARL J.Probabilistic reasoning in intelligent systems:Net-works of plausible inference[M].Elsevier,2014.
[57]ZHANG H C,WANG J Y.Towards adversarially robust object detection [C]//Proceedings of IEEE/CVF International Confe-rence on Computer Vision(ICCV).Piscataway,NJ:IEEE,2020:421-430.
[58]CHEN S T,CORNELIUS C,MARTIN J,et al.Shapeshifter:Robust physical adversarial attack on Faster R-CNN object detector [C]//Joint European Conference on Machine Learning and Knowledge Discovery in Databases.Cham:Springer,2019:52-68.
[59]CARLINI N,WAGNER D.Towards evaluating the robustness of neural networks [C]//IEEE Symposium on Security and Privacy.2017:39-57.
[60]ATHALYE A,ENGSTROM L,ILYAS A,et al.Synthesizing robust adversarial examples [C]//International Conference on Machine Learning.2018:284-293.
[61]WANG D,LI C,WEN S,et al.Daedalus:Breaking nonmaximum suppression in object detection via adversarial examples [J].IEEE Transactions on Cybernetics,2021,52(8):7427-7440.
[62]WEI X,LIANG S,CHEN N,et al.Transferable adversarial attacks for image and video object detection [C]//Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence.2019:954-960.
[63]CHOW K H,LIU L,LOPER M,et al.Adversarial objectness gradient attacks in real-time object detection systems [C]//IEEE International Conference on Trust,Privacy and Security in Intelligent Systems and Applications.2020:263-272.
[64]WU X,HUANG L,GAO C,et al.G-UAP:Generic universal adversarial perturbation that fools RPN-based detectors [C]//Asian Conference on Machine Learning.2019:1204-1217.
[65]SHARMA G,GARG U.Unveiling vulnerabilities:evadingYOLOv5 object detection through adversarial perturbations and steganography [J].Multimedia Tools and Applications,2024,83:74281-74300.
[66]LI G,XU Y,DING J,et al.Toward generic and controllable attacks against object detection [J].IEEE Transactions on Geo-science and Remote Sensing,2024,62:1-12.
[67]THYS S,VAN RANST W,GOEDEME T.Fooling automated surveillance cameras:Adversarial patches to attack person detection [C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops.2019.
[68]HUANG L,GAO C,ZHOU Y,et al.Universal physical camouflage attacks on object detectors [C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2020:720-729.
[69]GUESMI A,BILASCO I M,SHAFIQUE M,et al.AdvART:Adversarial art for camouflaged object detection attacks [C]//2024 IEEE International Conference on Image Processing(ICIP).IEEE,2024:666-672.
[70]CUI J,GUO W,HUANG H,et al.Adversarial examples for vehicle detection with projection transformation [J].IEEE Transactions on Geoscience and Remote Sensing,2024,62:1-18.
[71]WEI X,YU J,HUANG Y.Infrared adversarial patches withlearnable shapes and locations in the physical world [J].International Journal of Computer Vision,2024,132:1928-1944.
[72]ZHU X,LIU Y,HU Z,et al.Physical adversarial attacks for infrared object detection [C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR).IEEE,2024:24284-24293.
[73]WANG X,LI W.Physical adversarial attacks for infrared object detection [C]//2024 4th International Conference on Consumer Electronics and Computer Engineering(ICCECE).IEEE,2024:64-69.
[74]HU C,SHI W,YAO W,et al.Adversarial infrared curves:Anattack on infrared pedestrian detectors in the physical world [J].Neural Networks,2024,178:106459.
[75]WANG Y,LI X,YANG L,et al.Adaptive oriented adversarial attacks on visible and infrared image fusion models [C]//2024 IEEE International Conference on Multimedia and Expo(ICME).IEEE,2024:1-6.
[76]TARCHOUN B,ALAM Q M,ABU-GHAZALEH N,et al.Fool the Hydra:Adversarial attacks against multi-view object detection systems [J].arXiv:2312.00173,2023.
[77]HA Q,WATANABE K,KARASAWA T,et al.MFNet:To-wards real-time semantic segmentation for autonomous vehicles with multi-spectral scenes [C]//2017 IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS).IEEE,2017:5108-5115.
[78]WOHLBERG B.Efficient algorithms for convolutional sparserepresentations [J].IEEE Transactions on Image Processing,2015,25(1):301-315.
[79]YUAN L,LI X M,PAN Z X,et al.A review of adversarial samples for object detection [J].Chinese Journal of Image and Graphics,2022,27(10):2873-2896.
[80]WEI X,HUANG Y,SUN Y,et al.Unified adversarial patch for visible-infrared cross-modal attacks in the physical world [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2024,46(4):2348-2363.
[81]CHEN P C,KUNG B H,CHEN J C.Class-aware robust adversarial training for object detection [C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR).Piscataway,NJ:IEEE,2021:10415-10424.
[82]DONG Z Y,WEI P X,LIN L.Adversarially-aware robust object detector [C]//European Conference on Computer Vision.Berlin:Springer,2022:297-313.
[83]CHIANG P Y,CURRY M J,ABDELKADER A,et al.Detection as regression:Certified object detection by Median smoothing [J].arXiv:2007.03730,2020.
[84]LI H F,LI G B,YU Y Z.ROSA:Robust salient object detection against adversarial attacks [J].IEEE Transactions on Cyberne-tics,2020,50(11):4835-4847.
[85]ZHOU G Z,GAO H C,CHEN P,et al.Information distribution based defense against physical attacks on object detection [C]//Proceedings of IEEE International Conference on Multimedia & Expo Workshops(ICMEW).Piscataway,NJ:IEEE,2020:1-6.
[86]CHIANG P H,CHAN C S,WU S H.Adversarial pixel mas-king:A defense against physical attacks for pre-trained object detectors [C]//Proceedings of the 29th ACM International Conference on Multimedia.New York:ACM,2021:1856-1865.
[87]WANG K,SHEN Y,LAUER M.Adversarial defense teacherfor cross-domain object detection under poor visibility conditions [J].arXiv:2403.15786,2024.
[88]STRACK L,WASEDA F,NGUYEN H H,et al.Defendingagainst physical adversarial patch attacks on infrared human detection [J].arXiv:2309.15519,2023.
[89]YU T,XUE Y,HE Y,et al.Adversarial defense technology for small infrared targets [J].Computers,Materials & Continua,2024,81(1):1235.
[90]ZHANG Y,ZHAO S,WEI X,et al.Defending adversarial patches via joint region localizing and inpainting [C]//Pattern Re-cognition and Computer Vision:7th Chinese Conference,PRCV 2024.Springer,2024:236-250.
[91]LEE H J,RO Y M.Adversarially robust multi-sensor fusionmodel training via random feature fusion for semantic segmentation [C]//2021 IEEE International Conference on Image Processing(ICIP).IEEE,2021:339-343.
[92]YU Y,LEE H J,KIM B C,et al.Towards robust training ofmulti-sensor data fusion network against adversarial examples in semantic segmentation [C]//ICASSP 2021-2021 IEEE International Conference on Acoustics,Speech and Signal Processing(ICASSP).IEEE,2021:4710-4714.
[93]LI J,YU H,CHEN J,et al.A²RNet:Adversarial Attack Resilient Network for Robust Infrared and Visible Image Fusion [C]//Proceedings of the AAAI Conference on Artificial Intelligence.2025:4770-4778.
[94]SUTTAPAK W,ZHANG J,ZHAO H,et al.Multi-Model U-Net:An Adversarial Defense Mechanism for Robust Visual Tracking [J].Neural Process Letters,2024,56:132.
[95]YUAN M,SHI X,WANG N,et al.Improving RGB-infrared object detection with cascade alignment-guided transformer [J].Information Fusion,2024,105:102246.
[96]ZHAO F,LOU W,FENG H,et al.MFMG-Net:MultispectralFeature Mutual Guidance Network for Visible-Infrared Object Detection [J].Drones,2024,8:112.
[97]WEI Z,YANG X,WANG N,et al.Dual-Adversarial Represen-tation Disentanglement for Visible Infrared Person Re-Identification [J].IEEE Transactions on Information Forensics and Security,2024,19:2186-2200.
[98]LIU J,FAN X,HUANG Z,et al.Target-aware dual adversarial learning and a multi-scenario multi-modality benchmark to fuse infrared and visible for object detection [C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2022:5802-5811.
[99]XU H,MA J,LE Z,et al.FusionDN:A unified densely connected network for image fusion [C]//Proceedings of the AAAI Conference on Artificial Intelligence.2020:12484-12491.
[100]LI C,LIANG X,LU Y,et al.RGB-T object tracking:Benchmark and baseline [J].Pattern Recognition,2019,96:106977.
[101]ZHANG X,YE P,XIAO G.VIFB:A visible and infrared image fusion benchmark [J].arXiv:2002.03322,2020.
[102]TOET A,HOGERVORST M A.Progress in color night vision [J].Optical Engineering,2012,51(1):010901.
[103]HWANG S,PARK J,KIM N,et al.Multispectral pedestrian de-tection:Benchmark dataset and baseline [C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2015:1037-1045.
[104]JIA X,ZHU C,LI M,et al.LLVIP:A visible-infrared paired dataset for low-light vision [C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.2021:3496-3504.
[105]FLIR.Free FLIR thermal dataset for algorithm training[EB/OL].https://www.flir.com/oem/adas/adas-dataset-form/.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

Survey of Adversarial Attack and Defense for RBG and Infrared Multimodal Object Detection

PDF (PC)

Abstract

Cite this article

share this article

References

Related Articles 15

Metrics

Comments

Recommended 0

[1]	YIN Shi, SHI Zhenyang, WU Menglin, CAI Jinyan, YU De. Deep Learning-based Kidney Segmentation in Ultrasound Imaging:Current Trends and Challenges [J]. Computer Science, 2025, 52(9): 16-24.
[2]	ZENG Lili, XIA Jianan, LI Shaowen, JING Maike, ZHAO Huihui, ZHOU Xuezhong. M2T-Net:Cross-task Transfer Learning Tongue Diagnosis Method Based on Multi-source Data [J]. Computer Science, 2025, 52(9): 47-53.
[3]	LI Yaru, WANG Qianqian, CHE Chao, ZHU Deheng. Graph-based Compound-Protein Interaction Prediction with Drug Substructures and Protein 3D Information [J]. Computer Science, 2025, 52(9): 71-79.
[4]	LUO Chi, LU Lingyun, LIU Fei. Partial Differential Equation Solving Method Based on Locally Enhanced Fourier NeuralOperators [J]. Computer Science, 2025, 52(9): 144-151.
[5]	LIU Leyuan, CHEN Gege, WU Wei, WANG Yong, ZHOU Fan. Survey of Data Classification and Grading Studies [J]. Computer Science, 2025, 52(9): 195-211.
[6]	LIU Wei, XU Yong, FANG Juan, LI Cheng, ZHU Yujun, FANG Qun, HE Xin. Multimodal Air-writing Gesture Recognition Based on Radar-Vision Fusion [J]. Computer Science, 2025, 52(9): 259-268.
[7]	LIU Zhengyu, ZHANG Fan, QI Xiaofeng, GAO Yanzhao, SONG Yijing, FAN Wang. Review of Research on Deep Learning Compiler [J]. Computer Science, 2025, 52(8): 29-44.
[8]	TANG Boyuan, LI Qi. Review on Application of Spatial-Temporal Graph Neural Network in PM_2.5 ConcentrationForecasting [J]. Computer Science, 2025, 52(8): 71-85.
[9]	SHEN Tao, ZHANG Xiuzai, XU Dai. Improved RT-DETR Algorithm for Small Object Detection in Remote Sensing Images [J]. Computer Science, 2025, 52(8): 214-221.
[10]	LIU Chengzhuang, ZHAI Sulan, LIU Haiqing, WANG Kunpeng. Weakly-aligned RGBT Salient Object Detection Based on Multi-modal Feature Alignment [J]. Computer Science, 2025, 52(7): 142-150.
[11]	XU Yongwei, REN Haopan, WANG Pengfei. Object Detection Algorithm Based on YOLOv8 Enhancement and Its Application Norms [J]. Computer Science, 2025, 52(7): 189-200.
[12]	ZHENG Cheng, YANG Nan. Aspect-based Sentiment Analysis Based on Syntax,Semantics and Affective Knowledge [J]. Computer Science, 2025, 52(7): 218-225.
[13]	CHEN Shijia, YE Jianyuan, GONG Xuan, ZENG Kang, NI Pengcheng. Aircraft Landing Gear Safety Pin Detection Algorithm Based on Improved YOlOv5s [J]. Computer Science, 2025, 52(6A): 240400189-7.
[14]	GAO Junyi, ZHANG Wei, LI Zelin. YOLO-BFEPS:Efficient Attention-enhanced Cross-scale YOLOv10 Fire Detection Model [J]. Computer Science, 2025, 52(6A): 240800134-9.
[15]	ZHANG Hang, WEI Shoulin, YIN Jibin. TalentDepth:A Monocular Depth Estimation Model for Complex Weather Scenarios Based onMultiscale Attention Mechanism [J]. Computer Science, 2025, 52(6A): 240900126-7.