Weathered Vision Enhancing Object Detection in Adverse Weather

Weathered Vision Enhancing Object Detection in Adverse Weather

Authors

  • Luv Surve
  • Hitesh Bhardwaj

Dataset

This very-high-resolution (VHR) remote sensing image dataset was constructed by Dr. Gong Cheng et al. from Northwestern Polytechnical University (NWPU). It includes:

  • 715 color images acquired from Google Earth with spatial resolution ranging from 0.5 to 2 meters.
  • 85 pansharpened color infrared images acquired from Vaihingen data with a spatial resolution of 0.08 meters.
  • In total, 800 VHR remote sensing images.

Each line of the corresponding text files defines a ground truth bounding box with the following coordinates:

(x1, y1) – [top-left coordinate]

(x2, y2) – [bottom-right coordinate]

The object classes are as follows:

  • 1 – Airplane
  • 2 – Ship
  • 3 – Storage Tank
  • 4 – Baseball Diamond
  • 5 – Tennis Court
  • 6 – Basketball Court
  • 7 – Ground Track Field
  • 8 – Harbor
  • 9 – Bridge
  • 10 – Vehicle

Model(s)

The model employed is YOLOv8 Medium (yolov8m.pt)

The YOLOv8 (You Only Look Once version 8) model is a state-of-the-art object detection model developed by Ultralytics. It is part of the YOLO (You Only Look Once) series, renowned for its speed and accuracy in real-time object detection tasks.

Version Information

  • Model: Ultralytics YOLOv8.2.91 🚀
  • Python Version: 3.10.12

Key Features

  • Enhanced Performance: YOLOv8.2.91 represents a significant improvement over previous YOLO versions, offering superior accuracy and faster inference times.
  • Real-Time Object Detection: The model is designed to detect objects in real-time, making it ideal for applications requiring immediate feedback, such as video surveillance and autonomous driving.
  • High Efficiency: YOLOv8 is optimized for both speed and performance, utilizing advanced algorithms and optimizations to deliver high-quality results efficiently.

Results

We have the following results:

Results Examples

Table Test 1

Test 1

Table Test 2

Test 2

Results Examples

Here are some examples of the results:

Cloudy Dense Cloudy Foggy
Cloudy Dense Cloudy Foggy
Night Rainy Snowy
Night Rainy Snowy