Abstract
The YOLO model, as an advanced real-time object detection algorithm, has made significant progress in the field of object detection since its inception. It achieves fast and accurate object detection by transforming the object detection task into a regression problem and using a single neural network to predict multiple bounding boxes and category probabilities. The YOLO model has significant advantages in processing speed, detection accuracy, and real-time performance, and is widely used in fields such as autonomous driving, intelligent monitoring, and medical image analysis. This paper adopts a tactile paving detection method based on YOLOv8 and CBAM attention mechanism, and improves the model's ability to extract tactile paving features by introducing CBAM attention mechanism and loss function. This method shows good adaptability when dealing with the tactile paving environment under different lighting and weather conditions, can effectively identify diverse obstacles, and provides a strong guarantee for the travel safety of the visually impaired. In conclusion, YOLO model has broad prospects in object detection and tactile paving detection. The tactile paving detection method based on YOLOv8 and CBAM attention mechanism adopted in this paper provides new ideas and methods for the development of tactile paving detection technology, and has important practical application value. In the future, with the continuous optimization of performance and technological innovation of YOLO series models, further improvement of hardware performance, and the fusion of YOLO and other sensor data to form a multimodal detection system, YOLO models are expected to play an important role in more practical application scenarios.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call