Multi-Step Sequence Flood Forecasting Based on MSBP Model

Zhang, Yue; Ren, Juanhui; Wang, Rui; Fang, Feiteng; Zheng, Wen

doi:10.3390/w13152095

Cited by 6 publications

(4 citation statements)

References 29 publications

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“…[ 43 ] enhances the YOLOv6 model by including several feature balances with finer resolution. The YOLOv6 model suggested in this study achieves a more symmetrical network structure by taking into account depth, breadth, and resolution, as previously proposed [ 43 , 44 , 45 ]. Detection of a YOLOv6 fire may be broken down into processes, as depicted in Figure 5 .…”

Section: Proposed Workmentioning

confidence: 96%

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

Saydirasulovich

Abdusalomov

Jamil

et al. 2023

Sensors

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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.

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Section: Proposed Workmentioning

confidence: 96%

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

Saydirasulovich

Abdusalomov

Jamil

et al. 2023

Sensors

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“…[22] improves the YOLO-V3 model and adds multiple feature scales with a smaller resolution, which is helpful for the identification of small flame regions. Inspired by [22][23][24], the Fire-YOLO model proposed in this paper is considered from the three dimensions of depth, width, and resolution, and realizes a more balanced network architecture. The steps of Fire-YOLO fire detection are shown in Figure 1.…”

Section: Fire-yolomentioning

confidence: 99%

Fire-YOLO: A Small Target Object Detection Method for Fire Inspection

et al. 2022

Self Cite

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For the detection of small targets, fire-like and smoke-like targets in forest fire images, as well as fire detection under different natural lights, an improved Fire-YOLO deep learning algorithm is proposed. The Fire-YOLO detection model expands the feature extraction network from three dimensions, which enhances feature propagation of fire small targets identification, improves network performance, and reduces model parameters. Furthermore, through the promotion of the feature pyramid, the top-performing prediction box is obtained. Fire-YOLO attains excellent results compared to state-of-the-art object detection networks, notably in the detection of small targets of fire and smoke. Overall, the Fire-YOLO detection model can effectively deal with the inspection of small fire targets, as well as fire-like and smoke-like objects. When the input image size is 416 × 416 resolution, the average detection time is 0.04 s per frame, which can provide real-time forest fire detection. Moreover, the algorithm proposed in this paper can also be applied to small target detection under other complicated situations.

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“…In the last two to three years, migration learning has shown extraordinary results in most computer vision tasks [21,22], and in today's practice, researchers rarely train deep learning models from scratch. Transfer learning, previously restricted to CV tasks, can now also be performed in the natural language processing domain, introducing recent language representation models [23][24][25], and the latest technology, Google's BERT [26]. Transfer learning performs well in natural language understanding tasks.…”

Section: Related Workmentioning

confidence: 99%

Recognition of Chinese Legal Elements Based on Transfer Learning and Semantic Relevance

Zhang

Wang

et al. 2022

Wireless Communications and Mobile Computing

Self Cite

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In recent years, LegalAI has rapidly attracted the attention of AI researchers and legal professionals alike. Elements of LegalAI are known as legal elements. These elements can bring intermediate supervisory information to the judicial trial task and make the model’s prediction results more interpretable. This paper proposes a Chinese legal element identification method based on BERT’s contextual relationship capture mechanism to identify the elements by measuring the similarity between legal elements and case descriptions. On the China Law Research Cup 2019 Judicial Artificial Intelligence Challenge (CAIL-2019) dataset, the final result improves 4.2 points over the method based on the BERT model but without using similarity metrics. This research method makes full use of the semantic information of text, which is essential in the judicial field of document processing.

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Multi-Step Sequence Flood Forecasting Based on MSBP Model

Cited by 6 publications

References 29 publications

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

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

Fire-YOLO: A Small Target Object Detection Method for Fire Inspection

Recognition of Chinese Legal Elements Based on Transfer Learning and Semantic Relevance

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