Semi-supervised wildfire smoke detection based on smoke-aware consistency

The semi-transparency property of smoke integrates it highly with the background contextual information in the image, which results in great visual differences in different areas. In addition, the limited annotation of smoke images from real forest scenarios brings more challenges for model training. In this paper, we design a semi-supervised learning strategy, named smokeaware consistency (SAC), to maintain pixel and context perceptual consistency in different backgrounds. Furthermore, we propose a smoke detection strategy with triple classification assistance for smoke and smoke-like object discrimination. Finally, we simplified the LFNet fire-smoke detection network to LFNet-v2, due to the proposed SAC and triple classification assistance that can perform the functions of some specific module. The extensive experiments validate that the proposed method significantly outperforms state-of-the-art object detection algorithms on wildfire smoke datasets and achieves satisfactory performance under challenging weather conditions.Peer ReviewedPostprint (published version

Fireground location understanding by semantic linking of visual objects and building information models

This paper presents an outline for improved localization and situational awareness in fire emergency situations based on semantic technology and computer vision techniques. The novelty of our methodology lies in the semantic linking of video object recognition results from visual and thermal cameras with Building Information Models (BIM). The current limitations and possibilities of certain building information streams in the context of fire safety or fire incident management are addressed in this paper. Furthermore, our data management tools match higher-level semantic metadata descriptors of BIM and deep-learning based visual object recognition and classification networks. Based on these matches, estimations can be generated of camera, objects and event positions in the BIM model, transforming it from a static source of information into a rich, dynamic data provider. Previous work has already investigated the possibilities to link BIM and low-cost point sensors for fireground understanding, but these approaches did not take into account the benefits of video analysis and recent developments in semantics and feature learning research. Finally, the strengths of the proposed approach compared to the state-of-the-art is its (semi -)automatic workflow, generic and modular setup and multi-modal strategy, which allows to automatically create situational awareness, to improve localization and to facilitate the overall fire understanding

A VLSI-oriented and power-efficient approach for dynamic texture recognition applied to smoke detection

The recognition of dynamic textures is fundamental in processing image sequences as they are very common in natural scenes. The computation of the optic flow is the most popular method to detect, segment and analyse dynamic textures. For weak dynamic textures, this method is specially adequate. However, for strong dynamic textures, it implies heavy computational load and therefore an important energy consumption. In this paper, we propose a novel approach intented to be implemented by very low-power integrated vision devices. It is based on a simple and flexible computation at the focal plane implemented by power-efficient hardware. The first stages of the processing are dedicated to remove redundant spatial information in order to obtain a simplified representation of the original scene. This simplified representation can be used by subsequent digital processing stages to finally decide about the presence and evolution of a certain dynamic texture in the scene. As an application of the proposed approach, we present the preliminary results of smoke detection for the development of a forest fire detection system based on a wireless vision sensor network.Junta de Andalucía (CICE) 2006-TIC-235