The welding quality of aircraft truss is an important guarantee of its working strength

so the effective detection and identification of truss weld defects is the focus of current aviation manufacturing industry. Aiming at the problems of complex calculation and low recognition accuracy existing in traditional target recognition methods

in order to detect the internal defects of weld quickly and effectively

a weld defect recognition method based on improved convolutional neural network (CNN) is proposed. Firstly

the threshold value of weld image is divided to make the feature information easier to extract. Then

an improved adaptive pooling method is designed

and a new model structure of weld image defect recognition is proposed

and the corresponding model parameters and calculation method are formulated. Finally

the recognition model is used to train and test the weld image. The results show that the network model can effectively realize the recognition and classification of weld defects

and the average correct recognition rate is 98.25%

which shows that the proposed method has the advantages of fast recognition speed

high accuracy and good robustness

and provides theoretical reference for the process of weld defect recognition.