• Optoelectronics Letters
  • Vol. 20, Issue 9, 568 (2024)
Zhihao GUO, Dongmei MA*, and Xiaoyun and LUO
Author Affiliations
  • School of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China
  • show less
    DOI: 10.1007/s11801-024-3241-z Cite this Article
    GUO Zhihao, MA Dongmei, and LUO Xiaoyun. A lightweight semantic segmentation algorithm integrating CA and ECA-Net modules[J]. Optoelectronics Letters, 2024, 20(9): 568 Copy Citation Text show less
    References

    [1] ASGARI T S, ABHISHEK K, COHEN J P, et al. Deep semantic segmentation of natural and medical images: a review[J]. Artificial intelligence review, 2021, 54(1): 137-178.

    [2] YU H, YANG Z, TAN L. Methods and datasets on semantic segmentation: a review[J]. Neurocomputing, 2018, 304: 82-103.

    [3] BI L, KIM J, AHN E. Dermoscopic image segmentation via multistage fully convolutional networks[J]. IEEE transactions on biomedical engineering, 2017, 64(9): 2065-2074.

    [4] SIDDIQUE N, PAHEDING S, ELKIN C P. U-Net and its variants for medical image segmentation: a review of theory and applications[J]. IEEE access, 2021, 9: 82031-82057.

    [5] YUAN W, WANG J, XU W. Shift pooling PSPNet: rethinking PSPNet for building extraction in remote sensing images from entire local feature pooling[J]. Remote sensing, 2022, 14(19): 4889.

    [6] YU D, XU Q, GUO H. An efficient and lightweight convolutional neural network for remote sensing image scene classification[J]. Sensors, 2020, 20(7): 1999.

    [7] CAO J, TIAN X, CHEN Z. Ancient mural segmentation based on a deep separable convolution network[J]. Heritage science, 2022, 10(1): 11.

    [8] ?ZTüRK C, TA?YüREK M, TüRKDAMAR M U. Transfer learning and fine‐tuned transfer learning methods’ effectiveness analyse in the CNN‐based deep learning models[J]. Concurrency and computation: practice and experience, 2023, 35(4): e7542.

    [9] LIN T Y, GOYAL P, GIRSHICK R. Focal loss for dense object detection[J]. IEEE transactions on pattern analysisand machine intelligence, 2020, 42(2): 318-327.

    [10] HOU Q, ZHOU D, FENG J. Coordinate attention for efficient mobile network design[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 20-25, 2021, Nashville, TN, USA. New York: IEEE, 2021: 9577301.

    [11] HAN G, ZHANG M, WU W. Improved U-Net based insulator image segmentation method based on attention mechanism[J]. Energy reports, 2021, 7: 210-217.

    [12] YANG Q, KU T, HU K. Efficient attention pyramid network for semantic segmentation[J]. IEEE access, 2021, 9: 18867-18875.

    [13] WEI H B, YUN J, JIA X L, et al. In-situ detection method of Jellyfish based on improved faster R-CNN and FP16[J]. IEEE access, 2023, 11: 81803-81814.

    [14] LI H, LU H, LI X. Mortar-FP8: morphing the existing FP32 infrastructure for high performance deep learning acceleration[J]. IEEE transactions on computer-aided design of integrated circuits and systems, 2023: 1-1.

    [15] CORDTS M, OMRAN M, RAMOS S, et al. The Cityscapes dataset for semantic urban scene understanding[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 2016: 7780719.

    [16] YONG L, MA L, SUN D, et al. Application of MobileNetV2 to waste classification[J]. PLOS one, 2023, 18(3): e0282336.

    [17] LU J, LEE S H, KIM I W, et al. Small foreign object detection in automated sugar dispensing processes based on lightweight deep learning networks[J]. Electronics,2023, 12(22): 4621.

    [18] WANG C, ZHONG C. Adaptive feature pyramid networks for object detection[J]. IEEE access, 2021, 9: 107024-107032.

    GUO Zhihao, MA Dongmei, and LUO Xiaoyun. A lightweight semantic segmentation algorithm integrating CA and ECA-Net modules[J]. Optoelectronics Letters, 2024, 20(9): 568
    Download Citation