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: The present invention discloses a method and an apparatus for obstacle detection under complex weather. The method includes: obtaining an image under a complex weather condition; performing enhanced preprocessing on the image by using a multi-scale retinex with color restoration MSRCR algorithm; inputting the preprocessed image into a trained obstacle detection model based on an improved YOLOv3 network; and according to output of the obstacle detection model based on the improved YOLOv3 network, determining an obstacle detection result under the complex weather; replacing a Leaky-ReLU activation function in convolutional layers in the original YOLOv3 network with an ELU activation function; and training the obstacle detection model with the processed data set to obtain a trained obstacle detection model based on the improved YOLOv3 network.

Abstract: The present disclosure discloses an image segmentation method combined with superpixel and multi-scale hierarchical feature recognition. This method is based on a convolutional neural network model taking multi-scale hierarchical features extracted from a Gaussian pyramid of an image as a recognition basis, and then being connected with a multilayer perceptron to achieve the recognition of each pixel in the image, moreover, this method is used tier performing superpixel segmentation on the image and is combined with a method for improving superpxiel in combination with LBP texture features to segment an original image so that an obtained superpixel block is more fitted to edges of targets, then, the original image is merged according to a mean value of a color, and finally, recognition of each target in the image is achieved.

Abstract: Disclosed are a parallel method and device for convolution computation and data loading of a neural network accelerator. The method needs two input feature maps and two convolution kernel cache blocks, and sequentially stores the input feature maps and 64 convolution kernels into cache sub-blocks according to a loading length, so as to execute convolution computation and simultaneously load data of a next group of 64 convolution kernels.