基于MFF-STDC网络的室外复杂环境小目标语义分割方法

doi:10.13229/j.cnki.jdxbgxb.20231299

Abstract

Abstract:

The MFF-STDC network model based on multi-level feature fusion is built in this paper to solve the problem that lightweight networks have weak segmentation effect for small target category objects in complex environment. Firstly，by superimposing the feature extraction module based on group convolution many times， the feature extraction capability of the network is improved. Secondly， the combination ability of multi-scale feature information is improved by hierarchical attention module and CA mechanism. Lastly，A-Cityscapes dataset A-IDD dataset and Field dataset were built based on adaptive replication algorithm， the number of small target categories in the dataset was increased， and training and testing were completed. The MFF-STDC network improves the mIoU by 4.01%， 3.65%， and 2.94% respectively comparing with the STDC， and segmentation of the small target categories in the complex environment is much better than that of other networks. A real-world testing experimental platform is built， and the test results show that the MFF-STDC network effectively improves the semantic segmentation accuracy and classification ability of small target categories， and meets the real-time requirements.

Key words: computer applications, small target category detection, multi-level feature fusion, coordinate attention mechanism, adaptive replication algorithm

CLC Number:

TP391

Qing-lin AI,Yuan-xiao LIU,Jia-hao YANG. Small target swmantic segmentation method based MFF-STDC network in complex outdoor environments[J].Journal of Jilin University(Engineering and Technology Edition), 2025, 55(8): 2681-2692.

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实际情况	预测结果
实际情况	正例	反例
正例	TP（真正例）	FN（假反例）
反例	FP（假正例）	TN（真反例）

模型	mIoU/%
模型	A-Cityscapes	A-IDD	Field
SegNet	44.36	30.72	29.49
ENet	57.93	43.12	38.64
BiSeNet	69.23	54.31	44.91
DeepLabV3+（MV2）	72.14	57.71	47.79
Segformer	71.32	56.13	47.12
STDC	71.85	56.34	46.84
本文网络（MFF-STDC）	75.86	59.99	49.78

模型	mIoU/%	picAcc/%	Params/M
SegNet	44.36	59.32	29.5
ENet	57.93	66.73	0.4
BiSeNet	69.23	78.65	59.24
DeepLabV3+（MV2）	71.64	83.82	45.57
Segformer	70.32	81.14	3.72
STDC	71.85	81.32	7.08
本文网络（MFF-STDC）	75.86	83.87	5.43

实验组别	DW-STDC	CA-FFM	HAM	MIC	mIoU/%	Params/M	FLOPs/G
Exp 1	×	×	×	×	71.85	8.57	16.95
Exp 2	√	×	×	×	72.67	5.36	13.72
Exp 3	×	√	×	×	72.31	8.576	16.953
Exp 4	×	×	√	×	72.09	8.561	16.95
Exp 5	×	×	×	√	72.35	8.661	17.702
Exp 6	√	√	√	√	75.86	5.43	14.48

分组数	各阶段模块层数			参数量 Params/M	计算量 Flops/G	精度 mIoU/%
分组数	Stage3	Stage4	Stage5	参数量 Params/M	计算量 Flops/G	精度 mIoU/%
1	2	2	2	8.57	16.95	71.85
C_out/16	2	2	2	4.34	12.03	71.33
C_out/16	4	5	3	5.36	13.72	72.67
C_out/16	7	9	5	7.08	16.26	73.02
C_out/4	1	2	2	4.43	12.20	70.89
C_out/4	2	4	3	5.51	14.08	71.98
C_out/4	2	8	6	7.35	16.90	72.42
C_out	1	2	2	4.34	12.03	69.83
C_out	2	4	3	5.36	13.72	71.04
C_out	2	8	6	7.08	16.26	71.78

Small target swmantic segmentation method based MFF-STDC network in complex outdoor environments

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