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Liu et al. Intell Robot 2024;4(4):503-23 I http://dx.doi.org/10.20517/ir.2024.29 Page 521

Provided technical guidance and reviewed the manuscript: Zhao W, Jia N
Reviewed the manuscript: Liu X, Yang J

Availability of data and materials
The raw data that support the findings of this study are available from the corresponding author Zhao W, upon
reasonablerequest. The datasets used inthepaperand othermodel-related imagedata havebeen appropriately
cited in References with the standard referencing conventions.

Financial support and sponsorship
This work was supported by the National Natural Science Foundation of China (62403361).

Conflicts of interest
All authors declared that there are no conflicts of interest.

Ethical approval and consent to participate
Not applicable.

Consent for publication
Not applicable.

Copyright
© The Author(s) 2024.

REFERENCES
1. Jia N, Chen Y, Liu X, Wang H. DIG: dual interaction and guidance network for salient object detection. Appl Intell 2023;53:28039–53. DOI
2. Itti L, Koch C, Niebur E. A model of saliency-based visual attention for rapid scene analysis. IEEE Trans Pattern Anal Mach Intell
1998;20:1254–9. DOI
3. Liu T, Yuan Z, Sun J, et al. Learning to detect a salient object. IEEE Trans Pattern Anal Mach Intell 2010;33:353–67. DOI
4. Tang G, Ni J, Shi P, Li Y, Zhu J. An improved ViBe-based approach for moving object detection. Intell Robot 2022;2:130-44. DOI
5. Geng Y, Lian Y, Yang S, Zhou M, Cao J. Reliable part guided multiple level attention learning for person re-identification. J Circuits Syst
Comput 2021;30:2150246. DOI
6. Ji A, Woo WL, Wong EWL, Quek YT. Rail track condition monitoring: a review on deep learning approaches. Intell Robot 2021;1:151–
75. DOI
7. Zhao KWG, Wang Y, Ma S, Lu J. SaliencyVR: saliency matching based visual relocalization for autonomous vehicle. IEEE Trans Intell
Veh 2024:1-10. DOI
8. Li X, Zhang T, Liu Z, et al. Saliency guided siamese attention network for infrared ship target tracking. IEEE Trans Intell Veh 2024:1-
18. DOI
9. Ding N, Zhang C, Eskandarian A. SalienDet: a saliency-based feature enhancement algorithm for object detection for autonomous driving.
IEEE Trans Intell Veh 2023;9:2624–35. DOI
10. Qin L, Shi Y, He Y, et al. ID-YOLO: real-time salient object detection based on the driver’s fixation region. IEEE Trans Intell Transp
Syst 2022;23:15898–908. DOI
11. Qian W, He Z, Chen C, Peng S. Navigating diverse salient features for vehicle re-identification. IEEE Trans Intell Transp Syst
2022;23:24578–87. DOI
12. Ravindran R, Santora MJ, Jamali MM. Multi-object detection and tracking, based on DNN, for autonomous vehicles: a review. IEEE
Sens J 2020;21:5668–77. DOI
13. Liu N, Han J, Yang MH. Picanet: learning pixel-wise contextual attention for saliency detection. In: Proceedings of the IEEE conference
on computer vision and pattern recognition; 2018. pp. 3089–98. DOI
14. Pang Y, Zhao X, Zhang L, Lu H. Multi-scale interactive network for salient object detection. In: 2020 IEEE/CVF Conference on Computer
Vision and Pattern Recognition (CVPR); 2020 Jun 13-19; Seattle, USA. IEEE; 2020. pp. 9413–22. DOI
15. Cheng MM, Gao SH, Borji A, Tan YQ, Lin Z, Wang M. A highly efficient model to study the semantics of salient object detection. IEEE
Trans Pattern Anal Mach Intell 2021;44:8006–21. DOI
16. Liu Y, Gu YC, Zhang XY, Wang W, Cheng MM. Lightweight salient object detection via hierarchical visual perception learning. IEEE
Trans Cybern 2020;51:4439–49. DOI
17. Liu Y, Zhang XY, Bian JW, Zhang L, Cheng MM. SAMNet: stereoscopically attentive multi-scale network for lightweight salient object
detection. IEEE Trans Image Process 2021;30:3804–14. DOI