Development And Application Of An Intelligent Learning Factory Safety Warning System Based On Yolov5 Algorithm

In today’s society,with the continuous development of the market economy and the rapid progress of modern science and technology,safety production has become the cornerstone of ensuring social stability and economic growth.In the process of vocational education in factories,workshop students may cause some unexpected industrial accidents due to their unfamiliarity with workshop operations and insufficient attention to safety operations,such as placing or discarding work tools at will.In order to ensure the safety and health of students,schools must take effective measures for prevention and response.Therefore,it is particularly important to develop a safety warning system that monitors the position and placement of work tools on the operation table in real time through artificial intelligence algorithms.This article starts from the current situation of vocational education,combines the needs of vocational education,conducts relevant research on factory workshops,and uses current popular development technologies and advanced target detection algorithms to develop a highly scalable workshop safety warning system with rich and practical functions.The main work of this article includes:(1)Building the YOLOv5-Conv Ne Xt target detection network,using grouped convolution to divide the input feature map into multiple groups,and then performing convolution operations within each group,which increases the model’s expression ability,effectively reduces the model’s parameter and calculation amount,and improves the model’s detection accuracy while maintaining the model’s detection efficiency.In order to capture as many feature regions of interest in the image as possible,the Sim AM attention mechanism is introduced to calculate the similarity between channels in the vector space,and then normalize and scale the similarity to obtain the weight of each channel,which does not require explicit learning of the global information of the feature map,reducing the model’s calculation amount.Finally,the model’s loss function is improved,and the EIOU loss function uses the specific difference value of width and height for calculation,replacing the aspect ratio in the CIOU loss function.At the same time,since EIOU introduces Focal Loss to solve the imbalance between difficult and easy samples,the model has better convergence speed and generalization ability,thereby improving the model’s performance.(2)Comparing the currently popular target detection algorithms,exploring the feasibility of the YOLOv5 target detection algorithm and improving it based on this algorithm,and completing the core functions of the safety warning system around this algorithm.A detailed architecture,functional module,and database design of the intelligent learning factory platform are provided.Based on the demand analysis and system design,the platform’s functional modules are developed and implemented,the intelligent learning factory platform’s functional requirements are completed,and the safety warning system is embedded to complete all functional businesses,and the effect diagram is displayed.(3)The developed intelligent learning factory system is matched with the workshop hardware and deployed,and the system is functionally tested through black box testing to ensure that the system achieves the specified target requirements,and the testing process is displayed.The system is performance tested using JMeter to perform multi-index testing on key businesses and high-traffic businesses to ensure system availability and good user experience.