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and Technology Development Center of the Ministry of Education Industry-university-Research Innovation Fund","award":["ZDYF2021GXJS003"],"award-info":[{"award-number":["ZDYF2021GXJS003"]}]},{"name":"Science and Technology Development Center of the Ministry of Education Industry-university-Research Innovation Fund","award":["ZDYF2020040"],"award-info":[{"award-number":["ZDYF2020040"]}]},{"name":"Science and Technology Development Center of the Ministry of Education Industry-university-Research Innovation Fund","award":["ZDKJ2020012"],"award-info":[{"award-number":["ZDKJ2020012"]}]},{"name":"Science and Technology Development Center of the Ministry of Education Industry-university-Research Innovation Fund","award":["62162022"],"award-info":[{"award-number":["62162022"]}]},{"name":"Science and Technology Development Center of the Ministry of Education Industry-university-Research Innovation Fund","award":["62162024"],"award-info":[{"award-number":["62162024"]}]},{"name":"Science and 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However, most of the current popular approaches have difficulty in obtaining sufficiently large receptive fields, and they sacrifice low-level details to improve inference speed, leading to decreased segmentation accuracy. In this paper, a lightweight and efficient multi-level feature adaptive fusion network (MFAFNet) is proposed to address this problem. Specifically, we design a separable asymmetric reinforcement non-bottleneck module, which designs a parallel structure to extract short- and long-range contextual information and use optimized convolution to increase the inference speed. In addition, we propose a feature adaptive fusion module that effectively balances feature maps with multiple resolutions to reduce the loss of spatial detail information. We evaluate our model with state-of-the-art real-time semantic segmentation methods on the Cityscapes and Camvid datasets. Without any pre-training and post-processing, our MFAFNet has only 1.27 M parameters, while achieving accuracies of 75.9% and 69.9% mean IoU with speeds of 60.1 and 82.6 FPS on the Cityscapes and Camvid test sets, respectively. The experimental results demonstrate that the proposed method achieves an excellent trade-off between inference speed, segmentation accuracy and model size.<\/jats:p>","DOI":"10.3390\/s23146382","type":"journal-article","created":{"date-parts":[[2023,7,14]],"date-time":"2023-07-14T00:49:30Z","timestamp":1689295770000},"page":"6382","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["MFAFNet: A Lightweight and Efficient Network with Multi-Level Feature Adaptive Fusion for Real-Time Semantic Segmentation"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7508-1445","authenticated-orcid":false,"given":"Kai","family":"Lu","sequence":"first","affiliation":[{"name":"School of Cyberspace Security (School of Cryptology), Hainan University, Haikou 570228, China"},{"name":"Department of Public Safety Technology, Hainan Vocational College of Politics and Law, Haikou 571100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jieren","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Hainan University, Haikou 570228, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hua","family":"Li","sequence":"additional","affiliation":[{"name":"School of Cyberspace Security (School of Cryptology), Hainan University, Haikou 570228, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianyu","family":"Ouyang","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, The University of Sydney, Camperdown, NSW 2006, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1016\/j.neunet.2022.10.034","article-title":"BASeg: Boundary aware semantic segmentation for autonomous driving","volume":"157","author":"Xiao","year":"2023","journal-title":"Neural Netw."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"107691","DOI":"10.1016\/j.compag.2023.107691","article-title":"ResDense-focal-DeepLabV3+ enabled litchi branch semantic segmentation for robotic harvesting","volume":"206","author":"Peng","year":"2023","journal-title":"Comput. 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