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Visibility is a complex phenomenon inspired by emissions and air pollutants or by factors, including sunlight, humidity, temperature, and time, which decrease the clarity of what is visible through the atmosphere. This paper provides a detailed overview of the state-of-the-art contributions in relation to visibility estimation under various foggy weather conditions. We propose VisNet, which is a new approach based on deep integrated convolutional neural networks for the estimation of visibility distances from camera imagery. The implemented network uses three streams of deep integrated convolutional neural networks, which are connected in parallel. In addition, we have collected the largest dataset with three million outdoor images and exact visibility values for this study. To evaluate the model’s performance fairly and objectively, the model is trained on three image datasets with different visibility ranges, each with a different number of classes. Moreover, our proposed model, VisNet, evaluated under dissimilar fog density scenarios, uses a diverse set of images. Prior to feeding the network, each input image is filtered in the frequency domain to remove low-level features, and a spectral filter is applied to each input for the extraction of low-contrast regions. Compared to the previous methods, our approach achieves the highest performance in terms of classification based on three different datasets. Furthermore, our VisNet considerably outperforms not only the classical methods, but also state-of-the-art models of visibility estimation.

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The technologies underlying fire and smoke detection systems play a crucial role in ensuring and delivering optimal performance in modern surveillance environments. In fact, fire can cause significant damage to lives and properties. Considering that the majority of cities have already installed camera-monitoring systems, this encouraged us to take advantage of the availability of these systems to develop cost-effective vision detection methods. However, this is a complex vision detection task from the perspective of deformations, unusual camera angles and viewpoints, and seasonal changes. To overcome these limitations, we propose a new method based on a deep learning approach, which uses a convolutional neural network that employs dilated convolutions. We evaluated our method by training and testing it on our custom-built dataset, which consists of images of fire and smoke that we collected from the internet and labeled manually. The performance of our method was compared with that of methods based on well-known state-of-the-art architectures. Our experimental results indicate that the classification performance and complexity of our method are superior. In addition, our method is designed to be well generalized for unseen data, which offers effective generalization and reduces the number of false alarms.

In the construction of new smart cities, traditional fire-detection systems can be replaced with vision-based systems to establish fire safety in society using emerging technologies, such as digital cameras, computer vision, artificial intelligence, and deep learning. In this study, we developed a fire detector that accurately detects even small sparks and sounds an alarm within 8 s of a fire outbreak. A novel convolutional neural network was developed to detect fire regions using an enhanced You Only Look Once (YOLO) v4network. Based on the improved YOLOv4 algorithm, we adapted the network to operate on the Banana Pi M3 board using only three layers. Initially, we examined the originalYOLOv4 approach to determine the accuracy of predictions of candidate fire regions. However, the anticipated results were not observed after several experiments involving this approach to detect fire accidents. We improved the traditional YOLOv4 network by increasing the size of the training dataset based on data augmentation techniques for the real-time monitoring of fire disasters. By modifying the network structure through automatic color augmentation, reducing parameters, etc., the proposed method successfully detected and notified the incidence of disastrous fires with a high speed and accuracy in different weather environments—sunny or cloudy, day or night. Experimental results revealed that the proposed method can be used successfully for the protection of smart cities and in monitoring fires in urban areas. Finally, we compared the performance of our method with that of recently reported fire-detection approaches employing widely used performance matrices to test the fire classification results achieved.

Authorities and policymakers in Korea have recently prioritized improving fire prevention and emergency response. Governments seek to enhance community safety for residents by constructing automated fire detection and identification systems. This study examined the efficacy of YOLOv6, a system for object identification running on an NVIDIA GPU platform, to identify fire-related items. Using metrics such as object identification speed, accuracy research, and time-sensitive real-world applications, we analyzed the influence of YOLOv6 on fire detection and identification efforts in Korea. We conducted trials using a fire dataset comprising 4000 photos collected through Google, YouTube, and other resources to evaluate the viability of YOLOv6 in fire recognition and detection tasks. According to the findings, YOLOv6’s object identification performance was 0.98, with a typical recall of 0.96 and a precision of 0.83. The system achieved an MAE of 0.302%. These findings suggest that YOLOv6 is an effective technique for detecting and identifying fire-related items in photos in Korea. Multi-class object recognition using random forests, k-nearest neighbors, support vector, logistic regression, naive Bayes, and XGBoost was performed on the SFSC data to evaluate the system’s capacity to identify fire-related objects. The results demonstrate that for fire-related objects, XGBoost achieved the highest object identification accuracy, with values of 0.717 and 0.767. This was followed by random forest, with values of 0.468 and 0.510. Finally, we tested YOLOv6 in a simulated fire evacuation scenario to gauge its practicality in emergencies. The results show that YOLOv6 can accurately identify fire-related items in real time within a response time of 0.66 s. Therefore, YOLOv6 is a viable option for fire detection and recognition in Korea. The XGBoost classifier provides the highest accuracy when attempting to identify objects, achieving remarkable results. Furthermore, the system accurately identifies fire-related objects while they are being detected in real-time. This makes YOLOv6 an effective tool to use in fire detection and identification initiatives.

Customer reviews on the Internet reflect users’ sentiments about the product, service, and social events. As sentiments can be divided into positive, negative, and neutral forms, sentiment analysis processes identify the polarity of information in the source materials toward an entity. Most studies have focused on document-level sentiment classification. In this study, we apply an unsupervised machine learning approach to discover sentiment polarity not only at the document level but also at the word level. The proposed topic document sentence (TDS) model is based on joint sentiment topic (JST) and latent Dirichlet allocation (LDA) topic modeling techniques. The IMDB dataset, comprising user reviews, was used for data analysis. First, we applied the LDA model to discover topics from the reviews; then, the TDS model was implemented to identify the polarity of the sentiment from topic to document, and from document to word levels. The LDAvis tool was used for data visualization. The experimental results show that the analysis not only obtained good topic partitioning results, but also achieved high sentiment analysis accuracy in document- and word-level sentiment classifications.