A Nighttime Driving-Scene Segmentation Method Based on Light-Enhanced Network
To solve the semantic segmentation problem of night driving-scene images, which often have low brightness, low contrast, and uneven illumination, a nighttime driving-scene segmentation method based on a light-enhanced network was proposed. Firstly, we designed a light enhancement network, which comp...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-10-01
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| Series: | World Electric Vehicle Journal |
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| Online Access: | https://www.mdpi.com/2032-6653/15/11/490 |
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| author | Lihua Bi Wenjiao Zhang Xiangfei Zhang Canlin Li |
| author_facet | Lihua Bi Wenjiao Zhang Xiangfei Zhang Canlin Li |
| author_sort | Lihua Bi |
| collection | DOAJ |
| description | To solve the semantic segmentation problem of night driving-scene images, which often have low brightness, low contrast, and uneven illumination, a nighttime driving-scene segmentation method based on a light-enhanced network was proposed. Firstly, we designed a light enhancement network, which comprises two parts: a color correction module and a parameter predictor. The color correction module mitigates the impact of illumination variations on the segmentation network by adjusting the color information of the image. Meanwhile, the parameter predictor accurately predicts the parameters of the image filter through the analysis of global content, including factors such as brightness, contrast, hue, and exposure level, thereby effectively enhancing the image quality. Subsequently, the output of the light enhancement network is input into the segmentation network to obtain the final segmentation prediction. Experimental results show that the proposed method achieves mean Intersection over Union (mIoU) values of 59.4% on the Dark Zurich-test dataset, outperforming other segmentation algorithms for nighttime driving-scenes. |
| format | Article |
| id | doaj-art-c7d926bd1b0a4c8290e9186a2fb2475d |
| institution | OA Journals |
| issn | 2032-6653 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj-art-c7d926bd1b0a4c8290e9186a2fb2475d2025-08-20T02:04:43ZengMDPI AGWorld Electric Vehicle Journal2032-66532024-10-01151149010.3390/wevj15110490A Nighttime Driving-Scene Segmentation Method Based on Light-Enhanced NetworkLihua Bi0Wenjiao Zhang1Xiangfei Zhang2Canlin Li3School of Software Engineering, Zhengzhou University of Light Industry, Zhengzhou 450000, ChinaSchool of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou 450000, ChinaSchool of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou 450000, ChinaSchool of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou 450000, ChinaTo solve the semantic segmentation problem of night driving-scene images, which often have low brightness, low contrast, and uneven illumination, a nighttime driving-scene segmentation method based on a light-enhanced network was proposed. Firstly, we designed a light enhancement network, which comprises two parts: a color correction module and a parameter predictor. The color correction module mitigates the impact of illumination variations on the segmentation network by adjusting the color information of the image. Meanwhile, the parameter predictor accurately predicts the parameters of the image filter through the analysis of global content, including factors such as brightness, contrast, hue, and exposure level, thereby effectively enhancing the image quality. Subsequently, the output of the light enhancement network is input into the segmentation network to obtain the final segmentation prediction. Experimental results show that the proposed method achieves mean Intersection over Union (mIoU) values of 59.4% on the Dark Zurich-test dataset, outperforming other segmentation algorithms for nighttime driving-scenes.https://www.mdpi.com/2032-6653/15/11/490semantic segmentationimage enhancementnighttime driving-scenedeep learning |
| spellingShingle | Lihua Bi Wenjiao Zhang Xiangfei Zhang Canlin Li A Nighttime Driving-Scene Segmentation Method Based on Light-Enhanced Network World Electric Vehicle Journal semantic segmentation image enhancement nighttime driving-scene deep learning |
| title | A Nighttime Driving-Scene Segmentation Method Based on Light-Enhanced Network |
| title_full | A Nighttime Driving-Scene Segmentation Method Based on Light-Enhanced Network |
| title_fullStr | A Nighttime Driving-Scene Segmentation Method Based on Light-Enhanced Network |
| title_full_unstemmed | A Nighttime Driving-Scene Segmentation Method Based on Light-Enhanced Network |
| title_short | A Nighttime Driving-Scene Segmentation Method Based on Light-Enhanced Network |
| title_sort | nighttime driving scene segmentation method based on light enhanced network |
| topic | semantic segmentation image enhancement nighttime driving-scene deep learning |
| url | https://www.mdpi.com/2032-6653/15/11/490 |
| work_keys_str_mv | AT lihuabi anighttimedrivingscenesegmentationmethodbasedonlightenhancednetwork AT wenjiaozhang anighttimedrivingscenesegmentationmethodbasedonlightenhancednetwork AT xiangfeizhang anighttimedrivingscenesegmentationmethodbasedonlightenhancednetwork AT canlinli anighttimedrivingscenesegmentationmethodbasedonlightenhancednetwork AT lihuabi nighttimedrivingscenesegmentationmethodbasedonlightenhancednetwork AT wenjiaozhang nighttimedrivingscenesegmentationmethodbasedonlightenhancednetwork AT xiangfeizhang nighttimedrivingscenesegmentationmethodbasedonlightenhancednetwork AT canlinli nighttimedrivingscenesegmentationmethodbasedonlightenhancednetwork |