Surface defect detection simulation of malleable iron based on convolutional neural network

To solve the problems that the existing detection algorithm of HRNetV2p could not balance the detection accuracy of defects at various scales, the feature fusion module combined with channel attention mechanism was introduced into the detection algorithm of HRNetV2p, which could adaptively adjust the ratio of spatial-semantic information in the fusion features and could improve the network ability to preserve semantic information in shallow features. The surface defect detection dataset of IIDD for malleable iron was established for data labelling and data statistics. The CG dense skip transmission unit and the CG adaptive fusion module were introduced into the HRNetV2p network for adaptively adjusting the spatial-semantic information ratio of the front-layer features through three operations of integration, recalibration and reintegration. The experimental setup and evaluation index were given, and the performance experiments of the improved HRNetV2p algorithm on the malleable iron surface defect dataset of IIDD were completed. The results show that the average detection accuracy AP₅₀ of the improved HRNetV2p algorithm on IIDD is 91.3%, which is 2.6% higher than the average detection accuracy of the original HRNetV2p. The detection accuracies of large, medium and small scale defects are improved by 2.7%, 2.7% and 5.6%, respectively.