Ecuadorian Traffic Officer Detection for Autonomous Vehicles in YOLOv8 format

Citation Author(s):
Juan P.
Ortiz
Universidad Politecnica Salesiana
Juan D.
Valladolid
Universidad Politecnica Salesiana
Submitted by:
Juan Ortiz
Last updated:
Sun, 06/02/2024 - 10:19
DOI:
10.21227/tczv-h556
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Abstract 

This is the collection of the Ecuadorian Traffic Officer Detection Dataset. This can be used mainly on Traffic Officer detection projects using YOLO. Dataset is in YOLO format. There are 1862 total images in this dataset fully annotated using  Roboflow Labeling tool.  Dataset is split as follow, 1734 images for training, 81 images for validation and 47 images for testing. Dataset is annotated only as one class-Traffic Officer (EMOV). The dataset produced a Mean Average Precision(mAP) of 96.4 % using YOLOv3m, 99.0 % using YOLOv5x  and 98.10 % using YOLOv8x. Also, increase in dataset and reducing the number of classes helped reduce training time.

Instructions: 

Dataset is in YOLO format. There are 1862 total images in this dataset fully annotated using  Roboflow labeling tool.  Dataset is annotated only as one class-Traffic Officer (EMOV).