XM-YOLOViT

Abstract:

A novel XM-YOLOViT real-time detection algorithm for pedestrians and vehicles in foggy days based on YOLOV5 framework is proposed, which effectively solves the problems of dense target interference and obscuration by haze, and improves the detection effect in complex foggy environments.

Papar Information

• Title: Research on real-time detection algorithm for pedestrian and vehicle in foggy weather based on lightweight XM-YOLOViT
• DOI: 10.1109/ACCESS.2023.3344666

Dataset Preparation

• Download

  Dataset	Download
  VisDrone-2019	download

• Generating Foggy Day Image Data

  Sampling atmospheric light values and generating foggy sky images using generating.py

  cd datasets
  python generating.py

  Tested Platform

• Software

  OS: Pop!_OS 22.04 LTS
  Python: 3.9.17 (miniconda)
  PyTorch: 1.13.1

• Hardware

  CPU: 12th Gen Intel(R) Core(TM) i7-12700H
  GPU: NVIDIA RTX3050 Laptop (4GB)

  Test Results

  mAP	F1-score