Complex network theory and topology analysis have been gradually applied in land transportation network research. However, the research on oil and gas storage and transportation in the port area is still blank. Based on the analysis of the operation process of the oil and gas storage and transportation system in the port area, this paper establishes the topological network structure of the oil and gas storage and transportation system for the first time. Through the analysis and calculation of parameters such as node degree, shortest path, and betweenness of nodes in this network, the key node analysis of the oil and gas storage and transportation system is realized.
Fire robots are an effective way to save lives from fire, but their limited detection accuracy has greatly hampered their practical applications in complicated fire conditions. This study therefore proposes an advanced thermal imaging flame detection model of YOLOv4-F based on YOLOv4-tiny. We replaced the Leaky ReLU activation function with the Mish activation function in the YOLOV4-tiny feature extraction network. A Spatial Pyramid Pooling (SPP) was also added to increase the receiving range of the feature extraction network. To improve the feature fusion efficiency between multi-scale feature layers, a Path Aggregation Network (PANet) was adopted to replace the YOLOv4-tiny Feature Pyramid Network (FPN) with full use of feature information; a high-quality dataset containing 14,757 thermal imaging flame images was built according to the PASCAL VOC 2007 dataset standard. The results show that, when compared to the YOLOv4-tiny, YOLOv5-s, and YOLOv7-tiny models, the average detection accuracy of the proposed YOLOv4-F model is 5.75% higher, the average mAP of the five IOU cases rises by 7.02%, and the average detection confidence of three scaled flames shows a 18.09% gain. The proposed YOLOV4-F meets the requirements of fire robots on real-time responses and accurate flame detection, offering an important tool to improve the performance of the current fire robots.
When a fire occurs in a building, the internal environment is full of dense smoke, which will greatly hinder the evacuation and rescue of the trapped persons. If the evacuation and rescue are not in time, the life safety of the trapped persons will be seriously threatened. In response to this problem, this paper proposes a method for quickly detecting trapped persons in building fires. This method uses a combination of multi-scale Retinex image sharpening algorithm and YOLOv4 person detection algorithm. First obtain the image information of the fire scene, use the multi-scale Retinex algorithm based on the Gaussian pyramid to perform the sharpening process, and then use the YOLOv4 model to perform the personnel detection on the sharpened fire scene image. The experimental results show that the confidence of image person detection after Retinex sharpening processing has been significantly improved.
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