YOLOv7-TS: A Traffic Sign Detection Model Based on Sub-Pixel Convolution and Feature Fusion

Zhao, Shan; Yuan, Yang; Wu, Xuan; Wang, Yunlei; Zhang, Fukai

doi:10.3390/s24030989

Cited by 3 publications

(1 citation statement)

References 49 publications

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“…For UAV object detection, [39] modifies YOLOv8 with Bi-PAN-FPN and improves 'C2F' with GhostblockV2. In [40], YOLOv7-TS devises a Feature Map Extraction Module to reduce information loss.…”

Section: The Yolo Seriesmentioning

confidence: 99%

A Convolution with Transformer Attention Module Integrating Local and Global Features for Object Detection in Remote Sensing Based on YOLOv8n

Lang,

Cui,

Yang

et al. 2024

Remote Sensing

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Object detection in remote sensing scenarios plays an indispensable and significant role in civilian, commercial, and military areas, leveraging the power of convolutional neural networks (CNNs). Remote sensing images, captured by crafts and satellites, exhibit unique characteristics including complicated backgrounds, limited features, distinct density, and varied scales. The contextual and comprehensive information in an image can make a detector precisely localize and classify targets, which is extremely valuable for object detection in remote sensing scenarios. However, CNNs, restricted by the essence of the convolution operation, possess local receptive fields and scarce contextual information, even in large models. To address this limitation and improve detection performance by extracting global contextual information, we propose a novel plug-and-play attention module, named Convolution with Transformer Attention Module (CTAM). CTAM is composed of a convolutional bottleneck block and a simplified Transformer layer, which can facilitate the integration of local features and position information with long-range dependency. YOLOv8n, a superior and faster variant of the YOLO series, is selected as the baseline. To demonstrate the effectiveness and efficiency of CTAM, we incorporated CTAM into YOLOv8n and conducted extensive experiments on the DIOR dataset. YOLOv8n-CTAM achieves an impressive 54.2 mAP@50-95, surpassing YOLOv8n (51.4) by a large margin. Notably, it outperforms the baseline by 2.7 mAP@70 and 4.4 mAP@90, showcasing its superiority with stricter IoU thresholds. Furthermore, the experiments conducted on the TGRS-HRRSD dataset validate the excellent generalization ability of CTAM.

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“…For UAV object detection, [39] modifies YOLOv8 with Bi-PAN-FPN and improves 'C2F' with GhostblockV2. In [40], YOLOv7-TS devises a Feature Map Extraction Module to reduce information loss.…”

Section: The Yolo Seriesmentioning

confidence: 99%

A Convolution with Transformer Attention Module Integrating Local and Global Features for Object Detection in Remote Sensing Based on YOLOv8n

Lang,

Cui,

Yang

et al. 2024

Remote Sensing

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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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Sign-YOLO: Traffic Sign Detection Using Attention-Based YOLOv7

Mahadshetti,

Kim,

2024

IEEE Access

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YOLOv7-TS: A Traffic Sign Detection Model Based on Sub-Pixel Convolution and Feature Fusion

Cited by 3 publications

References 49 publications

A Convolution with Transformer Attention Module Integrating Local and Global Features for Object Detection in Remote Sensing Based on YOLOv8n

A Convolution with Transformer Attention Module Integrating Local and Global Features for Object Detection in Remote Sensing Based on YOLOv8n

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

Sign-YOLO: Traffic Sign Detection Using Attention-Based YOLOv7

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