Analysis of reports published by the leading national centers for monitoring wildfires and other emergencies revealed that the devastation caused by wildfires has increased by 2.96-fold when compared to a decade earlier. The reports show that the total number of wildfires is declining; however, their impact on the wildlife appears to be more devastating. In recent years, deep neural network models have demonstrated state-of-the-art accuracy on many computer vision tasks. In this paper, we describe the design and implementation of a lightweight wildfire image classification model (LW-FIRE) based on convolutional neural networks. We explore different ways of using the existing dataset to efficiently train a deep convolutional neural network. We also propose a new method for dataset transformation to increase the number of samples in the dataset and improve the accuracy and generalization of the deep learning model. Experimental results show that the proposed model outperforms the state-of-the-art methods, and is suitable for real-time classification of wildfire images.

In the last decade, there have been many reports on the negative impact of wildfires on various ecosystems. Unfortunately, wildfires have been intensifying as global temperatures, droughts, and other instances of extreme weather events rise around the world. These circumstances are forcing communities to vigorously address the uncontrolled spread of wildfires, where the ultimate goal is the protection of wildlife. At the same time, many disaster prevention and monitoring methods, based on image processing and computer vision, have been developed. In this paper, we present a new unsupervised method based on RGB color space for the early detection of wildfires from still images. From the analysis of existing state-of-the-art methods, it is evident that different methods explore different color spaces for the extraction of flame features. Our motivation was to use only RGB color space and thus eliminate the time-consuming task of color space conversion. The proposed method consists of several new image processing techniques used to efficiently extract flame features. It outperforms the existing methods, where an increase of 3% and 2% is recorded in the F1 score and Matthews correlation coefficient, respectively. Such performance demonstrates the merits of the proposed method for flame segmentation and detection.

Efficient detection of distresses on asphalt pavements has a great impact on safe driving, thus it has been very active research subject in recent years. High severity level distresses, such as potholes, are the most severe threat to safe driving, hence timely detecting and repairing potholes is crucial in ensuring safety and quality of driving. Existing methods often require sophisticated equipment and algorithms with high-computational pre-processing steps for analysis of substantial amount of existing data (images or videos). In this paper, a new unsupervised method for detection of high severity distresses on asphalt pavements was proposed. The method was tested on highly unstructured image data set captured from different cameras and angles, with different irregular shapes and number of potholes to demonstrate its capability. Results indicated that the method can be used for rough detection and estimation of damaged pavements.

Superpixel segmentation is a very popular image segmentation technique used in various computer vision tasks. Recently, a number of superpixel algorithms have been proposed in literature. One such algorithm is considered as the-state-of-the-art in superpixel segmentation: Simple Linear Iterative Clustering or SLIC. However, its original implementation has a long execution time on high performance processors designed within the common mobile and enterprise applications, as well on high-end processors such as Intel Xeon. Overall, the execution time for single-threaded implementation is considered critical for real-time or near real-time applications. In this paper, we explore the possibility of accelerating parts of the SLIC image segmentation critical for performance, by designing the image segmentation accelerator for Intel's Arria 10 SoC. We propose a novel architecture to enable hardware acceleration by addressing the problem of hardware/software partitioning to minimize the overall program latency.