YOLOP-MVF: A Multi-Task Autonomous Driving Perception Detection Method Based on Multi Scale Feature Weighted Fusion
To address challenges such as large-scale variations, background interference, and occlusions in multi-task autonomous driving perception, this paper proposes YOLOP-MVF, a multi-task detection framework based on multi-scale feature weighting fusion. The model integrates a sub-pixel 3D fusion module...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11008744/ |
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| author | Yanqiu Niu Jing Zhang |
| author_facet | Yanqiu Niu Jing Zhang |
| author_sort | Yanqiu Niu |
| collection | DOAJ |
| description | To address challenges such as large-scale variations, background interference, and occlusions in multi-task autonomous driving perception, this paper proposes YOLOP-MVF, a multi-task detection framework based on multi-scale feature weighting fusion. The model integrates a sub-pixel 3D fusion module and a triple feature encoding module to enhance the representation of multi-scale features. A multi-scale convolutional attention-weighting mechanism is further introduced to adaptively emphasize critical spatial information. To improve feature extraction flexibility, deformable convolutions are incorporated, enabling dynamic sampling based on input characteristics. Additionally, the Powerful-IoU loss is employed to guide anchor box regression with adaptive penalty and gradient regulation, accelerating convergence. Experimental results on the BDD100K dataset demonstrate that YOLOP-MVF outperforms baseline models, achieving improvements of 1.2% in mIoU, 8.8% in accuracy, and 4.7% in mAP50, validating its effectiveness for robust multi-task perception in complex driving scenarios. |
| format | Article |
| id | doaj-art-83b3692788524cfd8de990398c3e08de |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-83b3692788524cfd8de990398c3e08de2025-08-20T03:24:39ZengIEEEIEEE Access2169-35362025-01-0113913749138310.1109/ACCESS.2025.357233111008744YOLOP-MVF: A Multi-Task Autonomous Driving Perception Detection Method Based on Multi Scale Feature Weighted FusionYanqiu Niu0https://orcid.org/0009-0009-8422-8988Jing Zhang1https://orcid.org/0000-0002-7531-7032Department of Basic Sciences, Jilin University of Architecture and Technology, Changchun, ChinaDepartment of Public Instruction, Anhui Vocational College of City Management, Hefei, ChinaTo address challenges such as large-scale variations, background interference, and occlusions in multi-task autonomous driving perception, this paper proposes YOLOP-MVF, a multi-task detection framework based on multi-scale feature weighting fusion. The model integrates a sub-pixel 3D fusion module and a triple feature encoding module to enhance the representation of multi-scale features. A multi-scale convolutional attention-weighting mechanism is further introduced to adaptively emphasize critical spatial information. To improve feature extraction flexibility, deformable convolutions are incorporated, enabling dynamic sampling based on input characteristics. Additionally, the Powerful-IoU loss is employed to guide anchor box regression with adaptive penalty and gradient regulation, accelerating convergence. Experimental results on the BDD100K dataset demonstrate that YOLOP-MVF outperforms baseline models, achieving improvements of 1.2% in mIoU, 8.8% in accuracy, and 4.7% in mAP50, validating its effectiveness for robust multi-task perception in complex driving scenarios.https://ieeexplore.ieee.org/document/11008744/Autonomous drivingmultitask learningdrivable area segmentationlane detectionvehicle detection |
| spellingShingle | Yanqiu Niu Jing Zhang YOLOP-MVF: A Multi-Task Autonomous Driving Perception Detection Method Based on Multi Scale Feature Weighted Fusion IEEE Access Autonomous driving multitask learning drivable area segmentation lane detection vehicle detection |
| title | YOLOP-MVF: A Multi-Task Autonomous Driving Perception Detection Method Based on Multi Scale Feature Weighted Fusion |
| title_full | YOLOP-MVF: A Multi-Task Autonomous Driving Perception Detection Method Based on Multi Scale Feature Weighted Fusion |
| title_fullStr | YOLOP-MVF: A Multi-Task Autonomous Driving Perception Detection Method Based on Multi Scale Feature Weighted Fusion |
| title_full_unstemmed | YOLOP-MVF: A Multi-Task Autonomous Driving Perception Detection Method Based on Multi Scale Feature Weighted Fusion |
| title_short | YOLOP-MVF: A Multi-Task Autonomous Driving Perception Detection Method Based on Multi Scale Feature Weighted Fusion |
| title_sort | yolop mvf a multi task autonomous driving perception detection method based on multi scale feature weighted fusion |
| topic | Autonomous driving multitask learning drivable area segmentation lane detection vehicle detection |
| url | https://ieeexplore.ieee.org/document/11008744/ |
| work_keys_str_mv | AT yanqiuniu yolopmvfamultitaskautonomousdrivingperceptiondetectionmethodbasedonmultiscalefeatureweightedfusion AT jingzhang yolopmvfamultitaskautonomousdrivingperceptiondetectionmethodbasedonmultiscalefeatureweightedfusion |