YOLOF-F: you only look one-level feature fusion for traffic sign detection

Wei, Hongyang; Zhang, Qianqian; Qin, Yugang; Li, Xiang; Qian, Yurong

doi:10.1007/s00371-023-02813-1

Cited by 9 publications

(2 citation statements)

References 36 publications

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“…The range of object detection is constrained by the dataset, requiring higher computational power, and still has some limitations in detecting small objects EfficientDet [36] Efficient and performance-balanced Relatively complex, requiring more computational resources. DETR [14] Introduces transformer, better handling of contextual information High computational resource demands, limited real-time capability.…”

Section: Model Pros Consmentioning

confidence: 99%

“…To address these challenges, researchers have recently proposed a plethora of innovative object detection algorithms, including DETR, EfficientDet, and YOLOv8. To begin with, the Transformerbased DETR model offers a novel perspective for object detection [14]. This model successfully integrates the self-attention mechanism of Transformers into object detection, facilitating more efficient processing of contextual information within images.…”

Section: Introductionmentioning

confidence: 99%

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Revolutionizing Target Detection in Intelligent Traffic Systems: YOLOv8-SnakeVision

Liu,

Lin

2023

Electronics

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Intelligent traffic systems represent one of the crucial domains in today’s world, aiming to enhance traffic management efficiency and road safety. However, current intelligent traffic systems still face various challenges, particularly in the realm of target detection. These challenges include adapting to complex traffic scenarios and the lack of precise detection for multiple objects. To address these issues, we propose an innovative approach known as YOLOv8-SnakeVision. This method introduces Dynamic Snake Convolution, Context Aggregation Attention Mechanisms, and the Wise-IoU strategy within the YOLOv8 framework to enhance target detection performance. Dynamic Snake Convolution assists in accurately capturing complex object shapes and features, especially in cases of target occlusion or overlap. The Context Aggregation Attention Mechanisms allow the model to better focus on critical image regions and effectively integrate information, thus improving its ability to recognize obscured targets, small objects, and complex patterns. The Wise-IoU strategy combines dynamic non-monotonic focusing mechanisms, aiming to more precisely regress target bounding boxes, particularly for low-quality examples. We validate our approach on the BDD100K and NEXET datasets. Experimental results demonstrate that YOLOv8-SnakeVision excels in various complex road traffic scenarios. It not only enhances small object detection but also strengthens the ability to recognize multiple targets. This innovative method provides robust support for the development of intelligent traffic systems and holds the promise of achieving further breakthroughs in future applications.

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Section: Model Pros Consmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Revolutionizing Target Detection in Intelligent Traffic Systems: YOLOv8-SnakeVision

Liu,

Lin

2023

Electronics

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AMDNet: Adaptive Fall Detection Based on Multi-scale Deformable Convolution Network

Jiang,

Zhang,

et al. 2024

Lecture Notes in Computer Science

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TSD‐YOLO: Small traffic sign detection based on improved YOLO v8

Du,

Pan,

et al. 2024

IET Image Processing

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Traffic sign detection is critical for autonomous driving technology. However, accurately detecting traffic signs in complex traffic environments remains challenge despite the widespread use of one‐stage detection algorithms known for their real‐time processing capabilities. In this paper, the authors propose a traffic sign detection method based on YOLO v8. Specifically, this study introduces the Space‐to‐Depth (SPD) module to address missed detections caused by multi‐scale variations of traffic signs in traffic scenes. The SPD module compresses spatial information into depth channels, expanding the receptive field and enhancing the detection capabilities for objects of varying sizes. Furthermore, to address missed detections caused by complex backgrounds such as trees, this paper employs the Select Kernel attention mechanism. This mechanism enables the model to dynamically adjust its focus and more effectively concentrate on key features. Additionally, considering the uneven distribution of training data, the authors adopted the WIoUv3 loss function, which optimizes loss calculation through a weighted approach, thereby improving the model's detection performance across various sizes and frequencies of instances. The proposed methods were validated on the CCTSDB and TT100K datasets. Experimental results demonstrate that the authors’ method achieves substantial improvements of 3.2% and 5.1% on the mAP50 metric compared to YOLOv8s, while maintaining high detection speed, significantly enhancing the overall performance of the detection system. The code for this paper is located at https://github.com/dusongjie/TSD‐YOLO‐Small‐Traffic‐Sign‐Detection‐Based‐on‐Improved‐YOLO‐v8

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YOLOF-F: you only look one-level feature fusion for traffic sign detection

Cited by 9 publications

References 36 publications

Revolutionizing Target Detection in Intelligent Traffic Systems: YOLOv8-SnakeVision

Revolutionizing Target Detection in Intelligent Traffic Systems: YOLOv8-SnakeVision

AMDNet: Adaptive Fall Detection Based on Multi-scale Deformable Convolution Network

TSD‐YOLO: Small traffic sign detection based on improved YOLO v8

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