# Intelligent Monitoring of Tunnel Fire Smoke Based on Improved YOLOX and Edge Computing
## 2 Feature Processing for Tunnel Flame Smoke Image  
### Improved Convolutional Neural Network Based on Wavelet Transform  
The intelligent detection of tunnel fires falls within the field of object detection in computer vision. In image processing, high-frequency data typically correspond to color variations and are often found at interfaces between foreground and background objects. In contrast, low-frequency data pertain to color information within objects. Therefore, some researchers apply Fourier transformation to convert images from the time domain to the frequency domain, allowing for the extraction of information that may be challenging to identify in the time-space domain by filtering low and high-frequency components in these signals. Convolutional neural networks (CNNs) are a type of deep learning model that employ convolution operations to extract features from images or signals, which are then input to classifiers and other modules for tasks such as classification and regression. Convolutions are typically performed in both horizontal and vertical directions within the image, involving the input image and convolutional kernels, which effectively capture patterns in the image's pixel values and generate feature maps. In the field of signal processing, convolution and multiplication are common operations; time-domain convolution reflects interactions between signals, while frequency-domain multiplication represents interactions in the frequency representation. Therefore, this paper introduces a CNN that incorporates wavelet transformation to enhance the accuracy of target detection by combining frequency domain analysis with convolutional neural networks. The wavelet transform, a signal processing technique, decomposes signals into a linear combination of various wavelet functions to extract different frequency components. Unlike the Fourier transform, the wavelet transform exhibits both temporal and spectral locality, making it particularly effective in characterizing non-stationary signals. The fundamental concept behind the wavelet transform involves representing signals using a set of wavelet functions. One specific method, the Haar transform, builds upon wavelet analysis and serves as a one-dimensional or two-dimensional orthogonal transformation. This transform efficiently converts signals or images from the time domain into the frequency domain. The Haar transform breaks down the signal or image into different scales, with each scale being described by a fundamental function. These fundamental functions, referred to as Haar wavelet bases, are derived from a generating function, each associated with a unique scale and translation. This simplicity enables Haar transform calculations through basic operations such as addition, subtraction, and shifting. In one-dimensional scenarios, the Haar transform divides the signal into two-unit blocks, applying addition and subtraction operations to generate a set of high-frequency and low-frequency coefficients. In two-dimensional contexts, the Haar transform decomposes images into four smaller blocks, with each block undergoing a process similar to the one-dimensional case. The Haar transform offers several advantages, including multi-resolution capabilities, localized analysis, and rapid computation. As a result, it finds extensive application in signal and image processing, encompassing tasks such as image compression, denoising, and feature extraction. In this paper, we employ the two-dimensional Haar transform to conduct low-pass and high-pass filtering operations along the horizontal, vertical, and diagonal directions. For an image with the dimensions \(N=2^{m}\times 2^{n}\), the i-th layer's decomposition output is determined using the method outlined by [PERSON] et al. [33]:\[