Research On The Working Condition Identification Method Of Mine Electromechanical Equipment Based On Deep Learning

With the rapid development of deep learning,target detection recognition technology has made significant breakthroughs and become a hot spot for research in the field of computer vision.Traditional target detection algorithms are susceptible to external factors such as light changes,occlusion and scale changes in the special environment of mines,which make the target detection accuracy lower and the detection effect unsatisfactory.The use of deep learning models has improved the target detection accuracy,but the consequent increasing cost of arithmetic power has led to the inability of the models to achieve real-time detection of targets on devices with low arithmetic power.For this reason,in this thesis,a lightweight target detection algorithm is constructed for the complex environment of mines to achieve high-precision real-time detection and recognition of the working status of mine electromechanical equipment,and it is applied on real mine data.The main research works are as follows.(1)The basic principles and operation processes of traditional target detection algorithms and convolutional neural network-based target detection algorithms are described,and their advantages and disadvantages are studied and analyzed.Through the comparative analysis of the performance of four classical algorithms,the design strategy of mine electromechanical equipment condition detection and identification is determined.(2)To address the problems of excessive parameter size,high leakage rate and false detection in the SSD model in the process of mine target detection,the depth-separable convolution and linear bottleneck structure in the MobileNet-V2 model are used in the base feature extraction network of SSD to replace the original VGG16 network to complete the pruning of the model and realize the lightweight of the model;the penalty function of Gaussian weighting is used The confidence decay function in the NMS algorithm is improved to improve the detection performance of the model.The experimental results show that the amount of model parameters of this algorithm is reduced by 86.6M,the detection speed is improved by 62FPS,and the problems of missed detection and false detection are effectively improved.(3)To address the problem of insufficient feature extraction capability of the model in the special environment of the mine,the feature extraction network of the SSD model is improved by using the FPN network structure to realize the fusion of high-level semantic information and low-level spatial location information;the aspect ratio of the default box of the model is optimized by using the K-means++algorithm to make the size of the default box of the model more consistent with the size of the real target in the homemade dataset.Improve the learning ability and detection recognition ability of the model for the gate switch state of mine electromechanical equipment,button indicator state,digital meter display state of equipment,and overall characteristics of pointer meters,and complete the reading of pointer meters by combining with traditional algorithms.The experimental results show that the multi-scale feature fusion of SSD electromechanical equipment working state detection and recognition algorithm designed in this thesis improves the mAP by 1.5%over the SSD target detection algorithm.This thesis optimizes the SSD target detection algorithm in terms of both detection accuracy and detection speed,and designs a lightweight algorithm model for mine electromechanical equipment working condition detection and recognition,which has a great degree of improvement in recognition accuracy and detection speed compared to the SSD algorithm.