Transport YOLO Robot — Pipeline de vision par ordinateur
Python pipeline for data preparation, training, and object detection for a robotic transportation system.
Transport YOLO Robot is a computer vision project that allows merging multiple YOLO format datasets and training custom detectors. It recognizes road signs, traffic lights, and the additional class Bart Simpson, while keeping the weights, metrics, and training graphs.
Problem addressed
The necessary data for detection is spread across multiple datasets using different classes and identifiers. The project must standardize them in order to train a single detector capable of recognizing multiple road elements and an additional visual class.
Objectives
Architecture
The project uses two Python scripts to merge images and annotations from multiple datasets. fusionner_datasets.py generates dataset_final with the classes stop, vitesse_80, and feux_rouge. fusionner_v2.py generates dataset_v2 with the classes stop, vitesse_80, feux_rouge, feux_vert, feux_orange, and bart_simpson. The scripts entrainer.py and entrainer_v2.py then train two custom detectors for 50 and 80 epochs, respectively. The weights, metrics, confusion matrices, and curves are saved in runs/detect/. The local application pc_main.py performs visual inference, detects QR codes with OpenCV, and uses speech synthesis. However, it currently loads the generic model yolov8n.pt and not the custom weights runs/detect/best_2/weights/best.pt.