Attention-Guided Multi-Scale Fusion Network for Similar Objects Semantic Segmentation-Reference-Cited by-同舟云学术

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Attention-Guided Multi-Scale Fusion Network for Similar Objects Semantic Segmentation

Published:2023-10-12 Issue:1 Volume:16 Page:366-376
ISSN:1866-9956
Container-title:Cognitive Computation
language:en
Short-container-title:Cogn Comput

Author:

Yao Fengqin,Wang Shengke^ORCID,Ding Laihui,Zhong Guoqiang,Li Shu,Xu Zhiwei

Funder

National Key Research and Development Program of China

Natural Science Foundation of Shandong Province

Science and Technology Program of Qingdao

Publisher

Springer Science and Business Media LLC

Subject

Cognitive Neuroscience,Computer Science Applications,Computer Vision and Pattern Recognition

Link

https://link.springer.com/content/pdf/10.1007/s12559-023-10206-8.pdf

Reference42 articles.

1. Amarasingam N, Salgadoe ASA, Powell K, Gonzalez LF, Natarajan S. A review of UAV platforms, sensors, and applications for monitoring of sugarcane crops. Remote Sensing Applications: Society and Environment. 2022;26:100712.

2. Yao F, Wang S, Ding L, Zhong G, Bullock LB, Xu Z, Dong J. Lightweight network learning with zero-shot neural architecture search for UAV images. Knowledge-Based Systems 2023;260:110142.

3. Delavarpour N, Koparan C, Nowatzki J, Bajwa S, Sun X. A technical study on UAV characteristics for precision agriculture applications and associated practical challenges. Remote Sens. 2021;13(6):1204.

4. Liao YH, Juang JG. Real-time UAV trash monitoring system. Appl Sci. 2022;12(4):1838.

5. del Cerro J, Cruz Ulloa C, Barrientos A, de León Rivas J. Unmanned aerial vehicles in agriculture: a survey. Agronomy. 2021;11(2):203.

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