Road Recognition Method of Photovoltaic Plant Based on Improved DeepLabv3+

doi:10.16183/j.cnki.jsjtu.2022.224

Abstract

Abstract:

Aiming at the problem that mobile cleaning robot needs to identify road accurately and quickly when it operates in photovoltaic plants, a target recognition model of improved DeepLabv3+ to identify the roads within photovoltaic plants is proposed. First, the backbone network of the original DeepLabv3+ model is replaced with an optimized MobileNetv2 network to reduce complexity. Then, the strategy that combines diverse receptive field fusion with depth separable convolution is employed, which enhances the atrous spatial pyramid pooling (ASPP) structure and improves the information utilization of ASPP and the training efficiency of model. Finally, the attention mechanism is introduced to improve the segmentation accuracy of the model. The results show that the average pixel accuracy of the improved model is 98.06%, and the average intersection over union is 95.92%, which are 1.79 percentage points and 2.44 percentage points higher than those of the DeepLabv3+ basic model, and SegNet and UNet models. Furthermore, the improved model has fewer parameters and a good real-time performance, which can better realize the road recognition of mobile cleaning robot of photovoltaic plants.

Key words: photovoltaic plants, road recognition, DeepLabv3+ model, attention mechanism, MobileNetv2

CLC Number:

TP242.6

LI Cuiming, WANG Hua, XU Longer, WANG Long. Road Recognition Method of Photovoltaic Plant Based on Improved DeepLabv3+[J]. Journal of Shanghai Jiao Tong University, 2024, 58(5): 776-782.

Figures/Tables 9

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References 15

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输入	网络层	输出步长	t	c	n	s	r
224×224×3	conv2d	2	—	32	1	2	1
112×112×32	bottleneck	2	1	16	1	1	1
112×112×16	bottleneck	4	6	24	2	2	1
56×56×24	bottleneck	8	6	32	3	2	1
28×28×32	bottleneck	16	6	64	4	2	1
28×28×64	bottleneck	16	6	96	3	1	1
14×14×96	bottleneck	16	6	160	3	1	2
7×7×160	bottleneck	16	6	320	1	1	4

模型	MPA/%	MIoU/%	单张图片推理时间/ms	总参数量× 10^-6
SegNet	93.84	91.42	121	14.86
UNet	94.73	92.05	125	17.30
原始Deeplabv3+	96.27	93.48	156	41.25
改进Deeplabv3+	98.06	95.92	112	2.28