RGE-YOLO enables lightweight road packaging bag detection for enhanced driving safety
Abstract Foreign objects such as packaging bags on the road pose a significant threat to driving safety, especially at high speeds or under low-visibility conditions. However, research on detecting road packaging bags remains limited, and existing object detection models face challenges in small obj...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-03240-z |
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| _version_ | 1849705336458444800 |
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| author | Dangfeng Pang Zhiwei Guan Tao Luo Yanhao Liang Ruzhen Dou |
| author_facet | Dangfeng Pang Zhiwei Guan Tao Luo Yanhao Liang Ruzhen Dou |
| author_sort | Dangfeng Pang |
| collection | DOAJ |
| description | Abstract Foreign objects such as packaging bags on the road pose a significant threat to driving safety, especially at high speeds or under low-visibility conditions. However, research on detecting road packaging bags remains limited, and existing object detection models face challenges in small object detection, computational efficiency, and embedded deployment. To address these issues, the lightweight deep learning model RGE-YOLO is the foundation for the real-time detection technique proposed in this contribution. Built upon YOLOv8s, RGE-YOLO incorporates RepViTBlock, Grouped Spatial Convolution (GSConv), and Efficient Multi-Scale Attention (EMA) to optimize computational efficiency, model stability, and detection accuracy. GSConv reduces redundant computations, enhancing model lightweight; EMA enhances the model’s ability to capture multi-scale information by integrating channel and spatial attention mechanisms; RepViTBlock integrates convolution and self-attention mechanisms to improve feature extraction capabilities. The proposed method was validated on a custom-built road plastic bag dataset comprising 6,000 augmented images. Experimental results demonstrate that RGE-YOLO outperforms state-of-the-art models such as Single Shot MultiBox Detector (SSD) and Faster Region-based Convolutional Neural Network (Faster R-CNN) in terms of mean average precision (mAP 92.2%) and detection speed (250 FPS), while significantly reducing model parameters (9.1 M) and computational complexity (23.9 GFLOPs), increasing its suitability for installation on computerized systems within vehicles. It introduces an effective and lightweight approach for detecting road packaging bags and contributes to increased driving safety. |
| format | Article |
| id | doaj-art-7a94072d389642f69ea59a13a45faa84 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-7a94072d389642f69ea59a13a45faa842025-08-20T03:16:31ZengNature PortfolioScientific Reports2045-23222025-05-0115111810.1038/s41598-025-03240-zRGE-YOLO enables lightweight road packaging bag detection for enhanced driving safetyDangfeng Pang0Zhiwei Guan1Tao Luo2Yanhao Liang3Ruzhen Dou4School of Mechanical Engineering, Tianjin Sino-German University of Applied SciencesSchool of Automobile and Rail Transportation, Tianjin Sino-German University of Applied SciencesSchool of Mechanical Engineering, Tianjin Sino-German University of Applied SciencesSchool of Mechanical Engineering, Tianjin Sino-German University of Applied SciencesTianjin SOTEREA Automotive Technology Co., LtdAbstract Foreign objects such as packaging bags on the road pose a significant threat to driving safety, especially at high speeds or under low-visibility conditions. However, research on detecting road packaging bags remains limited, and existing object detection models face challenges in small object detection, computational efficiency, and embedded deployment. To address these issues, the lightweight deep learning model RGE-YOLO is the foundation for the real-time detection technique proposed in this contribution. Built upon YOLOv8s, RGE-YOLO incorporates RepViTBlock, Grouped Spatial Convolution (GSConv), and Efficient Multi-Scale Attention (EMA) to optimize computational efficiency, model stability, and detection accuracy. GSConv reduces redundant computations, enhancing model lightweight; EMA enhances the model’s ability to capture multi-scale information by integrating channel and spatial attention mechanisms; RepViTBlock integrates convolution and self-attention mechanisms to improve feature extraction capabilities. The proposed method was validated on a custom-built road plastic bag dataset comprising 6,000 augmented images. Experimental results demonstrate that RGE-YOLO outperforms state-of-the-art models such as Single Shot MultiBox Detector (SSD) and Faster Region-based Convolutional Neural Network (Faster R-CNN) in terms of mean average precision (mAP 92.2%) and detection speed (250 FPS), while significantly reducing model parameters (9.1 M) and computational complexity (23.9 GFLOPs), increasing its suitability for installation on computerized systems within vehicles. It introduces an effective and lightweight approach for detecting road packaging bags and contributes to increased driving safety.https://doi.org/10.1038/s41598-025-03240-z |
| spellingShingle | Dangfeng Pang Zhiwei Guan Tao Luo Yanhao Liang Ruzhen Dou RGE-YOLO enables lightweight road packaging bag detection for enhanced driving safety Scientific Reports |
| title | RGE-YOLO enables lightweight road packaging bag detection for enhanced driving safety |
| title_full | RGE-YOLO enables lightweight road packaging bag detection for enhanced driving safety |
| title_fullStr | RGE-YOLO enables lightweight road packaging bag detection for enhanced driving safety |
| title_full_unstemmed | RGE-YOLO enables lightweight road packaging bag detection for enhanced driving safety |
| title_short | RGE-YOLO enables lightweight road packaging bag detection for enhanced driving safety |
| title_sort | rge yolo enables lightweight road packaging bag detection for enhanced driving safety |
| url | https://doi.org/10.1038/s41598-025-03240-z |
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