Enhancing the Image Pre-Processing for Large Fleets Based on a Fuzzy Approach to Handle Multiple Resolutions
Image pre-processing is crucial for large fleet management. Many traffic videos are collected by closed-circuit television (CCTV), which has a fixed area monitoring for image analysis. This paper adopts the front camera installed in large vehicles to obtain moving traffic images, whereas CCTV is mor...
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MDPI AG
2024-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/18/8254 |
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| author | Ching-Yun Mu Pin Kung |
| author_facet | Ching-Yun Mu Pin Kung |
| author_sort | Ching-Yun Mu |
| collection | DOAJ |
| description | Image pre-processing is crucial for large fleet management. Many traffic videos are collected by closed-circuit television (CCTV), which has a fixed area monitoring for image analysis. This paper adopts the front camera installed in large vehicles to obtain moving traffic images, whereas CCTV is more limited. In practice, fleets often install cameras with different resolutions due to cost considerations. The cameras evaluate the front images with traffic lights. This paper proposes fuzzy enhancement with RGB and CIELAB conversions to handle multiple resolutions. This study provided image pre-processing adjustment comparisons, enabling further model training and analysis. This paper proposed fuzzy enhancement to deal with multiple resolutions. The fuzzy enhancement and fuzzy with brightness adjustment produced images with lower MSE and higher PSNR for the images of the front view. Fuzzy enhancement can also be used to enhance traffic light image adjustments. Moreover, this study employed You Only Look Once Version 9 (YOLOv9) for model training. YOLOv9 with fuzzy enhancement obtained better detection performance. This fuzzy enhancement made more flexible adjustments for pre-processing tasks and provided guidance for fleet managers to perform consistent image-enhancement adjustments for handling multiple resolutions. |
| format | Article |
| id | doaj-art-57f76d68bb8a48549e808c2b6d4b08b6 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-57f76d68bb8a48549e808c2b6d4b08b62025-08-20T01:55:58ZengMDPI AGApplied Sciences2076-34172024-09-011418825410.3390/app14188254Enhancing the Image Pre-Processing for Large Fleets Based on a Fuzzy Approach to Handle Multiple ResolutionsChing-Yun Mu0Pin Kung1College of Construction and Development, Feng Chia University, Taichung 40724, TaiwanGIS Research Center, Feng Chia University, Taichung 40724, TaiwanImage pre-processing is crucial for large fleet management. Many traffic videos are collected by closed-circuit television (CCTV), which has a fixed area monitoring for image analysis. This paper adopts the front camera installed in large vehicles to obtain moving traffic images, whereas CCTV is more limited. In practice, fleets often install cameras with different resolutions due to cost considerations. The cameras evaluate the front images with traffic lights. This paper proposes fuzzy enhancement with RGB and CIELAB conversions to handle multiple resolutions. This study provided image pre-processing adjustment comparisons, enabling further model training and analysis. This paper proposed fuzzy enhancement to deal with multiple resolutions. The fuzzy enhancement and fuzzy with brightness adjustment produced images with lower MSE and higher PSNR for the images of the front view. Fuzzy enhancement can also be used to enhance traffic light image adjustments. Moreover, this study employed You Only Look Once Version 9 (YOLOv9) for model training. YOLOv9 with fuzzy enhancement obtained better detection performance. This fuzzy enhancement made more flexible adjustments for pre-processing tasks and provided guidance for fleet managers to perform consistent image-enhancement adjustments for handling multiple resolutions.https://www.mdpi.com/2076-3417/14/18/8254large fleetsmoving imagesmultiple resolutionspre-processingfuzzy enhancementYOLOv9 |
| spellingShingle | Ching-Yun Mu Pin Kung Enhancing the Image Pre-Processing for Large Fleets Based on a Fuzzy Approach to Handle Multiple Resolutions Applied Sciences large fleets moving images multiple resolutions pre-processing fuzzy enhancement YOLOv9 |
| title | Enhancing the Image Pre-Processing for Large Fleets Based on a Fuzzy Approach to Handle Multiple Resolutions |
| title_full | Enhancing the Image Pre-Processing for Large Fleets Based on a Fuzzy Approach to Handle Multiple Resolutions |
| title_fullStr | Enhancing the Image Pre-Processing for Large Fleets Based on a Fuzzy Approach to Handle Multiple Resolutions |
| title_full_unstemmed | Enhancing the Image Pre-Processing for Large Fleets Based on a Fuzzy Approach to Handle Multiple Resolutions |
| title_short | Enhancing the Image Pre-Processing for Large Fleets Based on a Fuzzy Approach to Handle Multiple Resolutions |
| title_sort | enhancing the image pre processing for large fleets based on a fuzzy approach to handle multiple resolutions |
| topic | large fleets moving images multiple resolutions pre-processing fuzzy enhancement YOLOv9 |
| url | https://www.mdpi.com/2076-3417/14/18/8254 |
| work_keys_str_mv | AT chingyunmu enhancingtheimagepreprocessingforlargefleetsbasedonafuzzyapproachtohandlemultipleresolutions AT pinkung enhancingtheimagepreprocessingforlargefleetsbasedonafuzzyapproachtohandlemultipleresolutions |