Transportation is an important link in the mining process, and large trucks are one of the important tools for mine transportation. Due to their large size and small driving position, large trucks have a blind spot, which is a hidden danger to the safe transportation of mines and has a great impact on production efficiency and economic loss. The traditional large truck safety warning system mainly uses the ultrasonic short-distance ranging method, radar ranging method, GPS (Global Positioning System) technology, and so on. The disadvantage of these methods is that they are affected by the environment and weather, and they cannot display the object status in real time. Therefore, it is becoming increasingly important to realize the large truck safety warning system based on machine vision. Therefore, this paper proposes a lightweight SSD (Single Shot MultiBox Detector) model and an atrous convolution to build a large-truck object recognition model. First, the training images are collected and marked. Then, the object recognition model is established by using the lightweight SSD model. The atrous convolutional layer is introduced to improve small object detection accuracy. In the end, the objectness prior method is used to improve the classification speed. Experimental results show that, compared with the original SSD model, the lightweight SSD model occupies less space and runs faster. The lightweight SSD model with the atrous convolutional layer is more sensitive to small objects and improves detection accuracy. The objectness prior method further improves the identification speed. Compared with the traditional large truck safety warning, the system is not affected by the environment and realizes the visualization of large truck safety warning.
Hydrometallurgy is a metallurgical method for processing complex ores and low-grade ores while reducing environmental pollution. The density of the thickening process in hydrometallurgical production is rather poor, and there are many interference factors, resulting in frequent failures in the density of the thickening process. The main focus of this paper is to propose a method of fault monitoring and diagnosis for the density of the thickening process in hydrometallurgy. First, through the support vector machine (SVM) algorithm, the fault detection model is established to monitor the blockage of the underflow pipeline of the thickener. Second, the fault diagnosis model is established by using the random forest algorithm, and particle swarm optimization is used to optimize the fault diagnosis model. The fault type is judged using the optimized diagnosis model, and the corresponding treatment measures are taken accordingly.INDEX TERMS Hydrometallurgy, density of thickening, support vector machines, particle swarm optimization, random forests, fault monitoring and fault diagnosis.
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