Problem Statement

“ Detect and classify the traffic sign object on image ”

Solution:

 Data Preprocessing: Since the dataset(Annotations) i recived was in tensorflow object detection format, i have to convert it to Yolov8 format. Why I Used YOLOv8? Here are a few main reasons why i consider using YOLOv8 for this work:

1. YOLOv8 has a high rate of accuracy measured by COCO and Roboflow 100.
2. YOLOv8 comes with a lot of developer-convenience features, from an easy-to- use CLI to a well-structured Python package.
3. There is a large community around YOLO and a growing community around the YOLOv8 model, meaning there are many people in computer vision circles who may be able to assist you when you need guidance.

YOLOv8 Architecture:

 Backbone: New CSP-Darknet53  Neck: SPPF, New CSP-PAN  Head: YOLOv3 Head Figure 1: YOLOv8 Architecture, visualisation made by GitHub user RangeKing Detection

YOLOv8 is an anchor-free model. This means it predicts directly the center of an object instead of the offset from a known anchor box.

Dataset information:

The dataset consist of 5539 images in total and which has 3 classes ( ADVISORY SPEED MPH, DIRECTIONAL ARROW AUXILIARY, DO NOT ENTER ). I have Split Dataset into Train, Test and Valid sets with 70, 20 and 10% respectively.Training And Results: In this work i have used nano pretrained model. And it gave good results in real time inference.

How to use this Repo:

In this work i have used nano pretrained model. And it gave good results in real time inference.

for trainig run python3 train_yolov8.py

for testing run python3 test_yolov8.py

Results: