Deep convolutional networks have better smoke recognition performance. However, a lightweight network model and high recognition accuracy cannot be balanced when deployed on hardware with limited computing resources such as edge computing. Based on this background, we propose a novel smoke recognition network that combines convolutional networks (CNN) and self-attention. The core ideas of this framework are as follows: (1) Combine the depthwise convolution and asymmetric convolution of large convolution kernels to construct a lightweight CNN model, and realize multiscale extraction of feature information with slight model complexity. (2) Combined with the self-attention in transformer, a skip-connection branch is designed, which improves the feature extraction capability of the backbone network through parallel processing and fusion of feature map information. (3) Fusion multicomponent discrete cosine transform (DCT) is used to compress channel information and expand the ability of global average pooling (GAP) to aggregate feature maps. The proposed DCT-GAP improves the accuracy of the network without adding additional computational costs. Experimental results show that the proposed CSANet achieves an average accuracy of over 98.3% with 238 M FLOPs and 5.8 M parameters on the homemade smoke dataset, outperforming state-of-the-art competitors.
For the single image dehazing problem, an end-to-end multi-stage dehazing algorithm is designed. The algorithm contains two distinct parts to extract features. For shallow features, texture-level information is mined by stacking pixel and channel attention mechanisms. The proposed method uses multi-head self-attention (MHSA) to capture high-level features. MHSA improves dehazing performance by mining the dependencies of a wide range of abstract information. The superiority of the transformer architecture is extended with cascaded attention mechanisms and convolutions to improve feature extraction capabilities. Multilayer perceptron (MLP) is used in the decoding stage to equalize the context information. Furthermore, a contrastive loss function that introduces multiple negative samples and correction terms is proposed. The correction term is generated according to the difference between the precise and blurred images, which can enhance the training effect when dealing with different concentrations of dehaze. The training result of this loss function assists the model in approximating clear images and staying away from blurry images. According to the experimental results compared with other methods under the same conditions, the proposed method achieves good results in both subjective visual effects and objective evaluation indicators. The proposed contrast loss function also improves the dehazing performance of the algorithm.
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