Emergency Analysis: Multitask Learning with Deep Convolutional Neural Networks for Fire Emergency Scene Parsing

Sharma, Jivitesh; Granmo, Ole‐Christoffer; Goodwin, Morten

doi:10.1007/978-3-030-79457-6_9

Cited by 5 publications

(3 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A CNN with five convolutional layers plus three fully linked layers was then utilized to automatically retrieve the highlights of candidate pixels. In addition, deep convolutional segmentation networks have been developed for analyzing fire emergency scenes, which focused on identifying and classifying items in an image based on their construction information, such as color, relatively high intensity compared to their surroundings, various shifts in form and size, and the items' propensity to catch fire [19].…”

Section: Related Workmentioning

confidence: 99%

An improved fire detection approach based on YOLO-v8 for smart cities

Talaat

ZainEldin

2023

Neural Comput & Applic

189

View full text Add to dashboard Cite

Fires in smart cities can have devastating consequences, causing damage to property, and endangering the lives of citizens. Traditional fire detection methods have limitations in terms of accuracy and speed, making it challenging to detect fires in real time. This paper proposes an improved fire detection approach for smart cities based on the YOLOv8 algorithm, called the smart fire detection system (SFDS), which leverages the strengths of deep learning to detect fire-specific features in real time. The SFDS approach has the potential to improve the accuracy of fire detection, reduce false alarms, and be cost-effective compared to traditional fire detection methods. It can also be extended to detect other objects of interest in smart cities, such as gas leaks or flooding. The proposed framework for a smart city consists of four primary layers: (i) Application layer, (ii) Fog layer, (iii) Cloud layer, and (iv) IoT layer. The proposed algorithm utilizes Fog and Cloud computing, along with the IoT layer, to collect and process data in real time, enabling faster response times and reducing the risk of damage to property and human life. The SFDS achieved state-of-the-art performance in terms of both precision and recall, with a high precision rate of 97.1% for all classes. The proposed approach has several potential applications, including fire safety management in public areas, forest fire monitoring, and intelligent security systems.

show abstract

Section: Related Workmentioning

confidence: 99%

An improved fire detection approach based on YOLO-v8 for smart cities

Talaat

ZainEldin

2023

Neural Comput & Applic

189

View full text Add to dashboard Cite

show abstract

“…A CNN with five convolutional layers plus three fully linked layers then automatically retrieved the candidate pixels’ highlights. Deep convolutional segmentation networks have been developed for analyzing fire emergency scenes, specifically for identifying and classifying items in an image based on their construction information regarding color, a relatively brilliant intensity compared to its surroundings, numerous shifts in form and size, and the items’ propensity to catch fire [ 19 ].…”

Section: Related Workmentioning

confidence: 99%

A YOLOv6-Based Improved Fire Detection Approach for Smart City Environments

Saydirasulovich

Abdusalomov

Jamil

et al. 2023

Sensors

View full text Add to dashboard Cite

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.

show abstract

“…Park et al [19] proposed ELASTIC-YOLOv3, a fire recognition method for urban environments that can quickly recognize a fire at night in urban areas by reflecting its nighttime characteristics. To analyze fire emergency scenes, Sharma et al [20] proposed a method that uses a deep learning image segmentation network to recognize objects based on their build material and vulnerability. Muhammad et al [21] integrated the principal component analysis as a preprocessing module with the improved YOLO-V3 to boost the network predictions for smoke in the wild.…”

Section: Smoke and Flame Recognition Algorithmsmentioning

confidence: 99%

Anchor-Free Smoke and Flame Recognition Algorithm with Multi-Loss

Chen

et al. 2023

Fire

View full text Add to dashboard Cite

Fire perception based on machine vision is essential for improving social safety. Object recognition based on deep learning has become the mainstream smoke and flame recognition method. However, the existing anchor-based smoke and flame recognition algorithms are not accurate enough for localization due to the irregular shapes, unclear contours, and large-scale changes in smoke and flames. For this problem, we propose a new anchor-free smoke and flame recognition algorithm, which improves the object detection network in two dimensions. First, we propose a channel attention path aggregation network (CAPAN), which forces the network to focus on the channel features with foreground information. Second, we propose a multi-loss function. The classification loss, the regression loss, the distribution focal loss (DFL), and the loss for the centerness branch are fused to enable the network to learn a more accurate distribution for the locations of the bounding boxes. Our method attains a promising performance compared with the state-of-the-art object detectors; the recognition accuracy improves by 5% for the mAP, 8.3% for the flame AP50, and 2.1% for the smoke AP50 compared with the baseline model. Overall, the algorithm proposed in this paper significantly improves the accuracy of the object detection network in the smoke and flame recognition scenario and can provide real-time fire recognition.

show abstract

Emergency Analysis: Multitask Learning with Deep Convolutional Neural Networks for Fire Emergency Scene Parsing

Cited by 5 publications

References 12 publications

An improved fire detection approach based on YOLO-v8 for smart cities

An improved fire detection approach based on YOLO-v8 for smart cities

A YOLOv6-Based Improved Fire Detection Approach for Smart City Environments

Anchor-Free Smoke and Flame Recognition Algorithm with Multi-Loss

Contact Info

Product

Resources

About