Shenzhen University

Yingying Zhu

Shihai Chen

Junqi Zhao

The present application relates to a smoke detection method based on deep learning, a device and a storage medium. The method includes the following steps: performing image enhancement processing and color transfer processing on a smoke image to obtain a color transferred smoke image; superimposing the color transferred smoke image on an indoor image to obtain an initial image, and performing screening processing, detection frame updating processing, feature extraction and layer calibration processing on the initial image to obtain a smoke calibrated feature image; performing segmentation processing, detection frame prediction processing, detection frame classification processing and image classification processing on the smoke calibrated feature image in sequence to obtain a smoke image set; and screening the smoke image set to obtain a target smoke image, and outputting a smoke detection result. The present disclosure realizes the collection of smoke data sets and improves the detection accuracy of smoke.

Smoke detection method based on deep learning, device and storage medium

Disclosed is a cross-view image optimizing method and apparatus, and a computer equipment and a readable storage medium. The method includes: acquiring a sample image and a pre-trained cross-view image generating model; generating an multi-dimensional cross-view image of the sample image by a multi-dimensional feature extracting module of the first generator to obtain dimension features and cross-view initial images at multiple dimensions; obtaining a multi-dimensional feature map with corresponding dimension features by the second generator; inputting the multi-dimensional feature map to a multi-channel attention module of the second generator for feature extraction and calculating a feature weight of each attention channel, obtaining attention feature images, attention images and feature weights in a preset number of the attention channels; and weighting and summing the attention images and the attention feature images of all the channels according to the feature weights, and obtaining a cross-view target image.

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Guangdong, China

Yingying Zhu