| Autor: |
FENG Wenbin, LI Shunan, TIAN Hao, YANG Xin, MA Chao, YU Chongchong |
| Jazyk: |
čínština |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Meikuang Anquan, Vol 53, Iss 2, Pp 136-141 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
1003-496X |
| DOI: |
10.13347/j.cnki.mkaq.2022.02.022 |
| Popis: |
Due to the poor underground environment, which makes the images of underground video in coal mines seriously degraded. However, the existing semantic segmentation model based on deep learning has the problem of fuzzy edge segmentation after image sharpness. A new method is proposed, which uses the fusion edge optimization module to process the boundary information and uses the gated convolution layer to connect the traditional feature extraction module to process the information in parallel. In order to supervise the learning of contour information, the binary cross entropy loss function was used to improve the learning effect, and the segmentation effect was optimized together with the loss function of conventional branches. The experimental results show that compared with other methods, the method based on the fusion edge optimization module improves the overall semantic segmentation accuracy and edge segmentation accuracy when the semantic segmentation task of the clarified coal mine image is performed. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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