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Remote Sensing
Volume 14
Issue 21
10.3390/rs14215315
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Article

RecepNet: Network with Large Receptive Field for Real-Time Semantic Segmentation and Application for Blue-Green Algae

by
Kaiyuan Yang
1,
Zhonghao Wang
1,
Zheng Yang
1,
Peiyang Zheng
1,
Shanliang Yao
1,
Xiaohui Zhu
1,*,
Yong Yue
1,
Wei Wang
2,
Jie Zhang
1 and
Jieming Ma
1
1
School of Advanced Technology, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China
2
College of Computer and Cyber Security, Hebei Normal University, Shijiazhuang 050024, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2022, 14(21), 5315; https://doi.org/10.3390/rs14215315
Submission received: 12 August 2022 / Revised: 15 October 2022 / Accepted: 17 October 2022 / Published: 24 October 2022
(This article belongs to the Special Issue Remote Sensing for Water Environment Monitoring)
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Abstract

Most high-performance semantic segmentation networks are based on complicated deep convolutional neural networks, leading to severe latency in real-time detection. However, the state-of-the-art semantic segmentation networks with low complexity are still far from detecting objects accurately. In this paper, we propose a real-time semantic segmentation network, RecepNet, which balances accuracy and inference speed well. Our network adopts a bilateral architecture (including a detail path, a semantic path and a bilateral aggregation module). We devise a lightweight baseline network for the semantic path to gather rich semantic and spatial information. We also propose a detail stage pattern to store optimized high-resolution information after removing redundancy. Meanwhile, the effective feature-extraction structures are designed to reduce computational complexity. RecepNet achieves an accuracy of 78.65% mIoU (mean intersection over union) on the Cityscapes dataset in the multi-scale crop and flip evaluation. Its algorithm complexity is 52.12 GMACs (giga multiply–accumulate operations) and its inference speed on an RTX 3090 GPU is 50.12 fps. Moreover, we successfully applied RecepNet for blue-green algae real-time detection. We made and published a dataset consisting of aerial images of water surface with blue-green algae, on which RecepNet achieved 82.12% mIoU. To the best of our knowledge, our dataset is the world’s first public dataset of blue-green algae for semantic segmentation.
Keywords: semantic segmentation; deep learning; real time; blue-green algae detection semantic segmentation; deep learning; real time; blue-green algae detection

Share and Cite

MDPI and ACS Style

Yang, K.; Wang, Z.; Yang, Z.; Zheng, P.; Yao, S.; Zhu, X.; Yue, Y.; Wang, W.; Zhang, J.; Ma, J. RecepNet: Network with Large Receptive Field for Real-Time Semantic Segmentation and Application for Blue-Green Algae. Remote Sens. 2022, 14, 5315. https://doi.org/10.3390/rs14215315

AMA Style

Yang K, Wang Z, Yang Z, Zheng P, Yao S, Zhu X, Yue Y, Wang W, Zhang J, Ma J. RecepNet: Network with Large Receptive Field for Real-Time Semantic Segmentation and Application for Blue-Green Algae. Remote Sensing. 2022; 14(21):5315. https://doi.org/10.3390/rs14215315

Chicago/Turabian Style

Yang, Kaiyuan, Zhonghao Wang, Zheng Yang, Peiyang Zheng, Shanliang Yao, Xiaohui Zhu, Yong Yue, Wei Wang, Jie Zhang, and Jieming Ma. 2022. "RecepNet: Network with Large Receptive Field for Real-Time Semantic Segmentation and Application for Blue-Green Algae" Remote Sensing 14, no. 21: 5315. https://doi.org/10.3390/rs14215315

APA Style

Yang, K., Wang, Z., Yang, Z., Zheng, P., Yao, S., Zhu, X., Yue, Y., Wang, W., Zhang, J., & Ma, J. (2022). RecepNet: Network with Large Receptive Field for Real-Time Semantic Segmentation and Application for Blue-Green Algae. Remote Sensing, 14(21), 5315. https://doi.org/10.3390/rs14215315

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