R-CNN-based [6] methods detect objects in two stages, which limited detection speed, making them less suitable for real-time traffic sign detection scenarios. In contrast to R-CNN-based methods, You ...

This project implements real-time object detection using YOLOv5. The model is capable of detecting and classifying objects in real-time from webcam input. YOLOv5 is a state-of-the-art, real-time ...

Current state-of-the-art oriented detectors have time-consuming feature extraction backbones, oriented proposals generation methods or additional special branches. These tricks increase the ...

To address these challenges, this paper introduces an enhanced YOLOv5 method for real-time UAV detection. In this approach, YOLOv5 serves as the base detector, with PANet neck and mosaic augmentation ...

This project showcases a real-time object detection system using YOLOv5, a top-tier deep learning model known for its speed and accuracy. By leveraging Python and popular libraries like OpenCV and ...

For example, YOLOv5 recommends more than 10,000 training instances per object class. The idea behind FOMO is that not all object-detection applications require the high-precision output that state ...