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제안 방법
- This paper proposes a fast and reliable smoke detection algorithm. The methodology and algorithm implementation will be described in section 2.
- In order to train the CNN network for smoke classification, we replaced the last layer with a new one trained from scratch using the back-propagation algorithm with our image dataset, which has only two different categories (smoke and non-smoke). The whole layers of cascade model work together as follows.
- This paper proposed a video-based smoke detection algorithm for use as an early fire-alarm system. The algorithm is based on detection of changes with background modeling, and cascade classification, and a deep learning convolution neural network. The cascade model helps by exploiting the advantages of multi-feature classification, and the success of CNNs in image classification promises high accuracy for smoke detection.
대상 데이터
- It is a trained CNN model for image classification[9]. The model was trained on the Image Net Database [15], which contains millions of images across 1,000 object categories. In this paper, we use the fine-tune approach in the CNN to retrain our image data for smoke classification.
이론/모형
- The model was trained on the Image Net Database [15], which contains millions of images across 1,000 object categories. In this paper, we use the fine-tune approach in the CNN to retrain our image data for smoke classification.
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