The realistic volume shadow of volumetric datasets can improve the perception of shape and depth, and further enhance the efficiency of detecting defects, anomalies, and other issues. In this paper, a novel and high-performance method called slice-based ray casting (SBRC) is proposed to implement the volume shadow of volumetric datasets. The first step of the SBRC method is to use the light source as the viewpoint to render the illumination information of each slice of the volumetric datasets, slice by slice, into the illumination attenuation buffer. The second step is to use the camera as the viewpoint, render volumetric datasets using ray casting, and calculate volume shadows using the illumination attenuation buffer. The experiments show that the method can obtain much better volume shadows and more scalable performance than other volume illumination methods. This is due to the illumination attenuation calculation slice by slice and the high-efficiency shadow calculation in ray casting. And, by using a genetic algorithm, we can optimize the shadow calculation parameters in CT images to make the edges in the images clearer.
Wildfires, also called forest fires, are a common natural disaster that often occur in forests and are difficult to control. Detecting and suppressing them at an early stage, primarily through monitoring smoke and fires, is crucial in reducing losses. Thanks to the efforts of researchers, wildfire detection technology has advanced significantly, from traditional manual monitoring to target detection, sensor detection, and infrared detection. However, the various detection methods still have some issues, including low accuracy, high costs, slow detection speeds and susceptibility to interference.
This paper presents an improved approach to identifying wildfires based on YOLOv7 with CBAM. Our experimental results indicate that our approach achieves a mean Average Precision (mAP) of 95.77%, surpassing VGG-SSD with CBAM by 0.53%, MobileNetv2-SSD with CBAM by 0.37%, and Faster R-CNN with CBAM by 2.1%. Thus, our method offers a highly accurate approach to wildfire identification.
Wildfire, also known as forest fire, is fire that usually occur in forests and are difficult to control. If it could be detected and suppressed at an early stage (mainly smoke and flames), it has important meaning for reducing the loss. With the attention of relevant researchers, wildfire detection technology has become more and more advanced, from traditional manual monitoring to traditional target detection to sensor detection and infrared detection, etc. The various detection methods involved still have problems such as slow detection speed, low accuracy, easy interference and high cost. In this paper, SSD, an advanced target detection method, was chosen from deep learning algorithms. Three independent SSD networks are built with VGG16, MobileNet v2, and EfficientNet b3 as the backbone. The experimental results show that the mAP (mean Average Precision) of VGG16-SSD is 95.34%, which is 4.76% higher than MobileNet v2-SSD and 4.53% higher than EfficientNet b3-SSD. Therefore, VGG16-SSD can effectively detect wildfires in the early stages.
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