Comment
Author: Admin | 2025-04-27
Also minimizes the cost of misclassification. IEC-MLP was used to evaluate the classification results of different breast cancer data sets, and the prediction results were good. Nilesh et al. [22] used a multilayer perceptron classifier based on artificial neural network to classify the tool state. The classification accuracy of MLP classifier is 97.33%. The results show that MLP gives higher classification accuracy. He et al. [23] proposed multi-scale MLP to aggregate adjacent patches with multi-scale shape to obtain rich spectral spatial information. In addition, soft MLP is proposed to further enhance the classification performance by applying soft segmentation operations. Finally, label smoothing is introduced to alleviate the over fitting problem in soft MLP (Soft MLP-L), which greatly improves the classification performance of MLP-based methods. Tang et al. [24] proposed a rice hyperspectral image classification model based on MLP network and residual learning. The results show that this model has higher classification accuracy than other commonly used classification models.Underground coal mines foreign object detection methodsAt the same time, many scholars have done research on foreign object detection of belt conveyor. Wu et al. [25] proposed a foreign object identification model of coal conveyor belt based on fast RCNN; By analyzing the characteristics of data transmission and target detection, Xu et al. [26] proposed the belt foreign object detection based on edge calculation and the target detection optimization algorithm suitable for edge equipment; Zhu et al. [27] proposed a foreign object recognition method of coal mine belt transportation based on deep learning, and improved the target detection method based on center net. Du et al. [28] proposed an enhanced YOLOv3 target detection model and applied it to coal mine conveyor foreign object detection, which improved the speed of foreign object detection while retaining a high detection accuracy.DiscussionIt is difficult for vision feature extraction and detection of foreign objects in the dusty and harsh environment of underground coal mine. And the original template matching algorithm is susceptible to the influence of light and the matching results are unstable for the fluctuant lighting condition. Therefore, an improved normalized cross-correlation template matching (NCC-TM) algorithm is proposed to effectively identify target objects with obscure shape features and fuzzy focus. Even if the target object is blocked for a small area, the detection result will not be affected. The proposed algorithm is demonstrated in the following sections.The proposed methodBasic theoryA. Retinex image enhancement. In the Retinex image
Add Comment