Outdoor Multi-object Scene Segmentation Method Based on DeepLabV3+

HAN Xiaozhen; TANG Zili; ZHANG Hua

doi:10.15892/j.cnki.djzdxb.2025.03.006

Journal of Projectiles, Rockets, Missiles and Guidance >

2025 , Vol. 45 >Issue 3: 311 - 317

DOI: https://doi.org/10.15892/j.cnki.djzdxb.2025.03.006

Outdoor Multi-object Scene Segmentation Method Based on DeepLabV3+

HAN Xiaozhen ,
TANG Zili ,
ZHANG Hua

Expand

No.63875 Unit, Huayin 714200,Shaanxi,China

Received date: 2025-02-18

Online published: 2025-07-09

Fold

Abstract

Image semantic segmentation assigns labels to each pixel in the target category based on the “semantics” of the image scene, distinguishing different types of things in the image. Existing semantic segmentation methods based on DeepLabV3+ have high computational complexity and large memory consumption, and it is difficult to fully utilize multi-scale information when extracting image feature information, which may lead to the loss of detailed information and reduce segmentation accuracy. The outdoor environment has a variety of targets, large changes in lighting conditions, and obstructions, which increase the difficulty of scene understanding and object recognition. Therefore, an improved DeepLabV3+ network outdoor multi-target scene segmentation method is proposed, with the improved MobileNetv2 as the model backbone. The ECAnet channel attention mechanism is applied to low-level features to reduce computational complexity and improve target boundary clarity. After the ASPP module, a polarized self-attention mechanism is introduced to improve the spatial feature representation of the feature mPA. The improved model has an average intersection to union ratio (mIoU) and average accuracy (mPA) of 69.5% and 82.35% on the outdoor dataset Standford BackgroundDataset, respectively. Compared with the original DeepLabV3+model, it has improved by 5.2% and 4.5%. The addition of new modules has no significant impact on the running time, effectively improving the inference efficiency and accuracy of the model.

Key words： DeepLabV3+; semantic segmentation; outdoor scene segmentation; multi-target segmentation

Cite this article

HAN Xiaozhen , TANG Zili , ZHANG Hua . Outdoor Multi-object Scene Segmentation Method Based on DeepLabV3+[J]. Journal of Projectiles, Rockets, Missiles and Guidance, 2025 , 45(3) : 311 -317 . DOI: 10.15892/j.cnki.djzdxb.2025.03.006

[an error occurred while processing this directive]

References

Publishing order | Descend order by publishing year | Descend order by cited within

[1]	张凯祥, 朱明. 基于YOLOv5的多任务自动驾驶环境感知算法[J]. 计算机系统应用, 2022, 31(9): 226-232. ZHANG K X, ZHU M. Multi-task autonomous driving environment perception algorithm based on YOLOv5[J] Computer System & Applications, 2022, 31(9): 226-232.

[2]	傅励瑶, 尹梦晓, 杨锋. 基于 Transformer 的 U 型医学图像分割网络综述[J]. 计算机应用, 2023, 43(5): 1584-1595. DOI FU L Y, YIN M X, YANG F. Transformer based U-shaped medical image segmentation network: a survey[J]. Journal of Computer Applications, 2023, 43(5): 1584-1595.

[3]	吴宁, 罗杨洋, 许华杰. 基于多尺度特征融合的遥感图像语义分割方法[J]. 计算机应用, 2024, 44(3): 737-744. DOI WU N, LUO Y Y, XU H J. Semantic segmentation method of remote sensing images based on multi-scale feature fusion[J]. Journal of Computer Applications, 2024, 44 (3): 737-744.

[4]	MULLER A C, NARAYANAN S. Cognitively engineered multisensor image fusion for military applications[J]. Information Fusion, 2009, 10(2): 137-149.

[5]	PENG B, ZHANG I. ZHANG D. A survey of graph theoretical approaches to image segmentation[J]. Pattern Recognition, 2013, 46(3): 1020-1038.

[6]	MARDIA K V, HAINSWORTH T J. A spatial thresholding method for image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1988, 10(6): 919-927.

[7]	ADAMS R, BISCHOF L. Seeded region growing[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1994, 16(6): 641-647.

[8]	ARBELAEZ P, MAIRE M, FOWLKES C C, et al. Contour detection and hierarchical image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(5): 898-916. DOI PMID

[9]	LAFFERTY J D, MCCALLUM A, PEREIRA F C N. Conditional random fields: probabilistic models for segmenting and labeling sequence data[C]// Anon. Proceedings of the Eighteenth International Conference on Machine Learning. Williamstown: Morgan Kaufmann Publishers Inc., 2001: 282-289.

[10]	刘磊, 石志国, 宿浩茹. 基于高阶马尔可夫随机场的图像分割[J]. 计算机研究与发展, 2013, 50(9): 1933-1942. LIU L, SHI Z G, SU H R. Image segmentation based on higher order Markov random field[J]. Journal of Computer Research and Development, 2013, 50(9): 1933-1942.

[11]	王博, 管永红, 刘洋. 基于改进SIFT的室内全景图像配准算法研究[J]. 电子设计工程, 2021, 29(6): 83-86. WANG B, GUAN Y H, LIU Y. Research on indoor panoramic image registration algorithm based on improved SIFT[J]. Electronic Design Engineering, 2021, 29(6): 83-86.

[12]	林克正, 张元铭, 李昊天. 信息熵加权的HOG特征提取算法研究[J]. 计算机工程与应用, 2020, 56(6): 147-152. DOI LIN K Z, ZHANG Y M, LI H T. Research on HOG feature extraction algorithm weighted by information entropy[J]. Computer Engineering and Applications, 2020, 56(6): 147-152. DOI

[13]	SHELHAMER E, LONG J, DARRELL T. Fully convolutional networks for semantic segmentation[C]// IEEE.Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2015: 3431-3440.

[14]	ZHAO H, SHI J, QI X, et al. Pyramid scene parsing network[C]// IEEE.Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2017: 6230-6239.

[15]	CHEN L C, PAPANDREOU G, KOKKINOS I, et al. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(4): 834-848.

[16]	CHEN L C, ZHU Y, PAPANDREOU G, et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[C]// ECVA.Proceedings of the 15th European Conference on Computer Vision. Cham: Springer, 2018: 833-851.

[17]	SANDLER M, HOWARD A, ZHU M, et al, MobileNetV2: inverted residuals and linear bottlenecks[C]// IEEE.Proceedings of the IEEE Conference on Computer Vision and Patterm Recognition. New York: IEEE, 2018: 4510-4520.

[18]	LONG J, SHELHAMER E, DARRELL T. Fully convolutional networks for semantic segmentation[C]// IEEE.Proceedings of the IEEE Conference on Computer Vision ahd Pattern Recognition. New York: IEEE, 2015: 3431-3140.

Options

Outlines