Generating Realistic Smoke Images With Controllable Smoke Components

Smoke image generation is an important method to solve sample categories imbalance in smoke detection with applications to forest smoke detection, smoky vehicles detection, etc. This article presents two Controllable Smoke Image Generation Neural Networks (CSGNet and CSGNet-v2). More specifically, CSGNet is proposed to generate various smoke images by integrating a smoke component control module (SCM) and a smoke image synthesis module (ISM) with a multi-scale strategy. By changing specified smoke components latent codes, we can generate smoke images with specified smoke components. By fine-tuning smoke components latent codes in SCM, we can fine-tune the smoke components in generated smoke images. To further improve CSGNet, CSGNet-v2 is proposed to make generated smoke images more realistic by adding a smoke image fine-tuning module (IFM). The experiment results show that our methods achieve by far the best results in generating smoke images with controllable smoke components.