Mar 4, 2025 · This paper reviews traditional traffic sign detection methods and introduces an enhanced detection algorithm (YOLO-BS) based on YOLOv8 (You Only Look Once version 8).

This project demonstrates the use of YOLOv8 for real-time sign language detection. It leverages a dataset from Roboflow Universe to train the model and achieve accurate detection of various sign language gestures.

This project uses a two-stage implementation for traffic sign recognition. On the first stage, real-time video stream from the cameras is processed by the trained YOLO model. Results are processed and bounding boxes are drawn around detections with …

Using and YOLOv5 for training and achieved >93% mAP on the dataset. Used WandB to log hyperparameters and output metrics from runs. Download the model.

A novel lightweight detection model based on YOLOv5s, namely Sign-YOLO, is proposed to overcome these challenges. Firstly, the CA (Coordinate Attention) module is incorporated into the backbone network to improve the extraction of key features.

To overcome these challenges, we propose Sign-YOLO (You Only Look Once), a novel attention-based one-stage method that integrates YOLOv7 with the squeeze-and-excitation (SE) model and special attention mechanism.

This project focuses on building an efficient Traffic Sign Recognition (TSR) system using the YOLOv8 model. Designed for real-time object detection, the model identifies and classifies traffic signs to enhance autonomous driving and smart traffic systems.

The YOLO-TS model integrates the Normalized Wasserstein Distance (NWD) with the Complete Intersection over Union (CIoU) loss function, significantly enhancing the detection of small traffic signs. The integration of StarBlock from StarNet into the C2f module forms the C2f-Star configuration, which simplifies the architecture.

YOLO (You Only Look Once) is a real-time object detection algorithm that has gained popularity due to its speed and accuracy [28]. Recent advancements in YOLO, such as YOLOv4, demonstrate the potential for even better performance [26].

We worked together to develop a robust traffic sign detection model using the YOLO framework. Our project leverages YOLO's real-time object detection capabilities to address the dynamic and time-sensitive nature of traffic environments.