Image-Adaptive-YOLO

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Image-Adaptive-YOLO is an open-source object detection model designed for adverse weather conditions. It utilizes image-adaptive filtering techniques to improve performance in challenging environments.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is Image-Adaptive-YOLO?

Image-Adaptive-YOLO is an open-source implementation of an object detection model specifically engineered to perform robustly in adverse weather conditions. Based on the research paper "Image-Adaptive YOLO for Object Detection in Adverse Weather Conditions (AAAI 2022)", this tool incorporates image-adaptive filtering techniques to enhance detection accuracy in scenarios like fog, darkness, or other challenging visual environments. The project provides code for installation, dataset preparation (including VOC PASCAL, RTTS, ExDark, and custom foggy/dark datasets), and both training and evaluation scripts. It is built on Python and TensorFlow, making it accessible for researchers and developers working on computer vision tasks in difficult conditions.

Best used for

Ideal for data scientists and professors who need to develop object detection systems robust to adverse weather, evaluate model performance in challenging conditions, and implement image-adaptive filtering techniques. Especially valuable for research and development in autonomous systems and surveillance where environmental robustness is critical.

Common actions

detect objects

improve image quality

train object detectors

evaluate models

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Capabilities

Key features

Image-adaptive filtering
Adverse weather object detection
YOLO-based model
TensorFlow implementation
Training scripts
Evaluation scripts

Target Audience

data scientistprofessor

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What kind of adverse weather conditions does Image-Adaptive-YOLO address?

Image-Adaptive-YOLO is designed to improve object detection performance in challenging visual conditions such as fog and low-light environments. It uses image-adaptive filtering to enhance visibility and detection accuracy where traditional YOLO models might struggle.

What are the primary dependencies for running Image-Adaptive-YOLO?

To run Image-Adaptive-YOLO, you will need Python 3 and TensorFlow installed. The project also specifies additional requirements that can be installed via `pip install -r ./docs/requirements.txt`, ensuring all necessary libraries are in place for development and execution.

Can I train Image-Adaptive-YOLO with my own datasets?

Yes, the project provides guidance on preparing datasets and references the `tensorflow-yolov3` implementation for more details on training with custom data. You can configure data paths in `core/config.py` to integrate your specific datasets for training and evaluation.

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Research & Education › Academic Research Data & Analytics › Predictive Analytics

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