Abstract: The present disclosure discloses a smoke detection system and a smoke detection method. The smoke detection system includes a camera, a storage unit, and a processor. The camera acquires a current image and a previous image. The storage unit stores a plurality of modules. The processor is coupled with the camera and executes the plurality of modules. The processor generates a difference image based on the current image and the previous image. The processor inputs the current image and the difference image to a semantic segmentation model so that the semantic segmentation model outputs a smoke confidence map. The smoke confidence map is generated based on whether a current environment is a dark environment or a bright environment. The processor analyzes the smoke confidence map to determine whether a smoke event occurs in the current image. Therefore, a reliable smoke detection function can be achieved.

Abstract: The present disclosure discloses a smoke detection system and a smoke detection method. The smoke detection system includes a camera, a storage unit, and a processor. The camera acquires a current image and a previous image. The storage unit stores a plurality of modules. The processor is coupled with the camera and executes the plurality of modules. The processor generates a difference image based on the current image and the previous image. The processor inputs the current image and the difference image to a semantic segmentation model so that the semantic segmentation model outputs a smoke confidence map. The smoke confidence map is generated based on whether a current environment is a dark environment or a bright environment. The processor analyzes the smoke confidence map to determine whether a smoke event occurs in the current image. Therefore, a reliable smoke detection function can be achieved.

Abstract: A system and method for adjusting input data of a decision-making neural network is provided, wherein the system includes a data-dividing neural network apparatus and a data processing apparatus. The data-dividing neural network apparatus receives an input data and divides the input data into a plurality of sub data including a first sub data and a second sub data. The data processing apparatus is coupled to the data-dividing neural network apparatus to receive the sub data, and process the first sub data and the second sub data by different ways when the sub data is processed, so that the first sub data and the second sub data are differently adjusted. The decision-making neural network is electrically coupled to the data processing apparatus to take the processed sub data as input data. As a result, the neural network can change the final output results.