Abstract: The present invention pertains to the technical field of smoke detection and discloses a lightweight fire smoke detection method, a terminal device, and a storage medium. Primarily, a smoke-like foreground is screened out based on a combination of a mixed Gaussian model and a YUV color model; and an ACON activation function is introduced to replace a Leaky ReLU activation function of YOLOv5 to form an ACON-CSP module for feature extraction. In the present invention, the smoke foreground is extracted for preprocessing by using the combination of the mixed Gaussian and YUV. Thus, in the preprocessing stage, static interference and non-smoke color interference in the preprocessing stage are ruled out while narrowing down the range of smoke detection, ensuring the relative accuracy and improving the detection speed, thereby providing a possible method for fire smoke detection of a low-end terminal device in an outdoor computer.

Abstract: Disclosed are an indoor passive human behavior recognition method and device. The method includes the following steps: dividing an indoor activity space into multiple regions, collecting a channel impulse response data packet of a reflection signal of each activity in each region to obtain an H (M, N, Z) matrix; preprocessing the H (M, N, Z) matrix to obtain a preprocessed H (M, N, Z) matrix; extracting features of the preprocessed H (M, N, Z) matrix to obtain a training sample of a convolutional neural network model; performing transfer learning on the convolutional neural network model using the training sample to obtain a trained convolutional neural network model; obtaining an indoor channel impulse response amplitude value, inputting the channel impulse response amplitude value into the trained convolutional neural network model, and outputting a human behavior.