Research On Solder Joint Identification And Location Of Circuit Board And Identification And Classification Of Common Electronic Components

With the development of machine vision and artificial intelligence technology,circuit board detection is developing towards intelligence and miniaturization.Aiming at the problem that the automatic detection system cannot accurately locate and detect the circuit board without the original design drawing,this paper studies the recognition and location of the solder joint of the circuit board and the recognition and classification of common electronic components.Through the application of traditional image processing and deep learning in the field of target detection,this paper improves the deep learning network model to ensure the detection accuracy and improve the detection speed at the same time,It realizes the identification and positioning of circuit board solder joints and the identification and classification of common electronic components.Based on the current situation of scientific research at home and abroad,this paper uses advanced technologies such as traditional image processing methods and deep learning target detection methods,and carries out scientific research on the basis of combining the improvement strategy of deep learning algorithm and detection standards.It mainly completes:(1)according to the image characteristics of circuit board solder joints,this paper uses the method of image processing to study the recognition and positioning of solder joints;(2)Through the research and comparison of target detection algorithms in common deep learning,the Yolo V4 algorithm is selected to realize the recognition and positioning of circuit board solder joints,which is compared with the implementation results of traditional image processing methods,and the advantages and disadvantages of the two detection methods are summarized;(3)In order to ensure the detection accuracy of the algorithm and improve the speed of the algorithm,the common lightweight algorithms are studied to improve the yoov4 algorithm.The self-made circuit board solder joint data set is used for training,and the training results are analyzed and compared to select the improved detection algorithm suitable for the target of the circuit board;(4)Using the improved algorithm selected,this paper makes an applied research on the recognition and classification of common electronic components on circuit board.Experiments show that this research realizes the detection of circular,rectangular and elliptical solder joints,and the recognition rate is more than 95%;The deviation between the detection coordinates of the solder joint and the real coordinates is less than 0.1mm;Using the improved yolov4 algorithm,the size of the model,the amount of parameters and the detection time are effectively reduced;The detection accuracy of common electronic components is more than 90%,and the map value is 83.4%;It can effectively identify and locate the solder joints of circuit board and identify and classify common electronic components.