Comment
Author: Admin | 2025-04-28
Of the multi-layer perceptron. The training flow chart is shown in Fig. 6.Fig. 6Multi-layer perceptron training processFull size imageFrameworkTo realize intelligent detection of the large foreign objects in coal mine conveyor, the proposed machine vision framework is shown in Fig. 7.Fig. 7Framework of visual detection system for large foreign objectsFull size imageIn the machine visual detection system, the industrial camera array is used to collect the coal conveying image and the image preprocessing (including the image filtering, denoising and image enhancement) is performed correspondingly. Then, the template matching is applied for preliminary identification of the foreign objects, and the qualified large foreign objects are screened out by combining the frame difference and area methods. If there is no large foreign object in the image, the image is cleared in the system to release storage memory. If large foreign objects are identified and screened out in the image, the texture features of the large foreign objects are extracted, and the optimized MLP is used to accurately classify the large foreign objects to prevent them entering the belt conveyor from the coal source. Specific detection process is shown in Fig. 8.Fig. 8Flowchart of large foreign objects detectionFull size imageImage enhancement using the improved MSR with adaptive weightThe SSR is difficult to balance the image color information and detail information, it cannot be applied to the application scenarios with harsh environments. The multi-scale Retinex (MSR) is proposed to solve this problem. The calculation formula of the MSR is described as follows.$$ \log [R_{{{\text{MSR}}}} (x,y)] = \sum\limits_{n = 1}^{N} {\omega_{n} \{ \log [S(x,y)] - \log [F_{n} (x,y)*S(x,y)]} \} , $$ (7) $$ \sum\limits_{n = 1}^{N} {\omega_{n} = 1} , $$ (8) where \(\omega_{n}\) is the weight coefficient and \(N\) represents the number of scales; when \(N = 1\), it is the case of single scale Retinex.Therefore, this paper proposes an improved MSR with adaptive weight. The principle and corresponding processing steps of the proposed method are described as follows: Step 1: use three Gaussian functions with different scale parameter values to perform the convolution operation with the three-color channels of the image, and calculate the weighted average value of the pixels in the Gaussian neighborhood. Step 2: in each color channel, move the Gaussian function by the distance of a Gaussian template from left to right and from top to bottom, and calculate the weighted average value of the pixels in the Gaussian template
Add Comment