(4) Deployment on Raspberry ... YOLOv5, YOLOv6, YOLOv8, YOLOv9, YOLOv10, and YOLOv11, respectively. Compared with the original RT-DETR model, mAP50 and mAP50:95 are improved by 1.5% and 13.2%, ...

ABSTRACT: This paper presents a comprehensive approach to face detection utilizing the YOLOv8 model, specifically trained on a diverse dataset consisting of images from four individuals. The trained ...

Integrating advanced object detection into your projects has become more accessible with the Raspberry Pi AI HAT ... and positional tracking. By using pre-built pipelines and GPIO components ...

As a list, you can see below: The circuit diagram need for this object detection project is very simple as there ... Next, you can use "Classify using YOLOv5" to recognize some of the common objects ...

Abstract: YOLOv5 is a popular object detection algorithm that is ... The article proposes an improved method for small object detection using YOLOv5s. First, a multilevel feature fusion detection head ...

This way, the sensitivity of using IoU alone to small objecet detection anchor box threshold changes was reduced. Furthermore, Convolutional Block ... the YOLOv5 model. It simultaneously enhances the ...

Abstract: A lightweight rotational object detection algorithm, R-YOLOv5, is proposed to address the limitations ... A cascaded Swin Transformer block (STrB) is used to reduce computational complexity ...