Research on vehicle detection based on improved YOLOX_S.

Aiming at the problem of easy misdetection and omission of small targets of long-distance vehicles in detecting vehicles in traffic scenes, an improved YOLOX_S detection model is proposed. Firstly, the redundant part of the original YOLOX_S network structure is clipped using the model compression strategy, which improves the model inference speed while maintaining the detection accuracy; secondly, the Resunit_CA structure is constructed by incorporating the coordinate attention module in the residual structure, which reduces the loss of feature information and improves the attention to the small target features; thirdly, in order to obtain richer small target features, the PAFPN structure tail to add an adaptive feature fusion module, which improves the model detection accuracy; finally, the loss function is optimized in the decoupled head structure, and the Focal Loss loss function is used to alleviate the problem of uneven distribution of positive and negative samples. The experimental results show that compared with the original YOLOX_S model, the improved model proposed in this paper achieves an average detection accuracy of 77.19% on this experimental dataset. However, the detection speed decreases to 29.73 fps, which is still a large room for improvement in detection in real-time. According to the visualization experimental results, it can be seen that the improved model effectively alleviates the problems of small-target missed detection and multi-target occlusion.

Rule-based expert system to assess caving output ratio in top coal caving.

Coal mining professionals in coal mining have recognized that the assessment of top coal release rate can not only improve the recovery rate of top coal, but also improve the quality of coal. But the process was often performed using a manual-based operation mode, which intensifies workload and difficulty, and is at risk of human errors. The study designs a assessment system to give the caving output ratio in top coal caving as accurately as possible based on the parameters adaptive Takagi-Sugeno (T-S) fuzzy system and the Levenberg-Marquardt (LM) algorithm. The main goal of the adaptive parameters based on LM algorithm is to construct its damping factor in the light of lowering of the objective function which is as taken as the index of termination iteration. The performance of the system is evaluated by Pearson correlation coefficient, Coefficient of Determination and relative error where the results of the Takagi-Sugeno method and the parameters adaptive Takagi-Sugeno method are compared to make the evaluation more robust and comprehensive.

Combination of minimum enclosing balls classifier with SVM in coal-rock recognition.

Top-coal caving technology is a productive and efficient method in modern mechanized coal mining, the study of coal-rock recognition is key to realizing automation in comprehensive mechanized coal mining. In this paper we propose a new discriminant analysis framework for coal-rock recognition. In the framework, a data acquisition model with vibration and acoustic signals is designed and the caving dataset with 10 feature variables and three classes is got. And the perfect combination of feature variables can be automatically decided by using the multi-class F-score (MF-Score) feature selection. In terms of nonlinear mapping in real-world optimization problem, an effective minimum enclosing ball (MEB) algorithm plus Support vector machine (SVM) is proposed for rapid detection of coal-rock in the caving process. In particular, we illustrate how to construct MEB-SVM classifier in coal-rock recognition which exhibit inherently complex distribution data. The proposed method is examined on UCI data sets and the caving dataset, and compared with some new excellent SVM classifiers. We conduct experiments with accuracy and Friedman test for comparison of more classifiers over multiple on the UCI data sets. Experimental results demonstrate that the proposed algorithm has good robustness and generalization ability. The results of experiments on the caving dataset show the better performance which leads to a promising feature selection and multi-class recognition in coal-rock recognition.