Defect detection for industrial neutron radiographic images based on modified YOLO network

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Neutron radiography (NR) has been widely used in non-destructive investigations. Since the industrial neutron radiographic images (NRIs) usually suffer from unclear defect features, insufficient data volume, and low efficiency of manual detection, we propose a proof-of-concept defect-detection method based on the modified YOLO network for the degraded NRIs in this paper. Firstly, 6336 radiographic images including crack, inclusion, blow hole, and residual core are built as the defect-detection dataset. Secondly, the types and the relative coordinates of defects are labeled by a graphical image annotation tool (i.e., Labelimg). Finally, the adaptive spatial feature fusion (ASFF) and convolutional block attention module (CBAM) are introduced to the modified YOLO network to enhance its ability of small-size defect detection. Experimental results demonstrate that the proposed method can achieve the average accuracy of 98.1% and detection rate of 85.985 frames per second (FPS) with a single NVIDIA GeForce RTX 4090 GPU on the built radiographic image dataset. In addition, the proposed method shows a good potential in detecting the above defects contained in the real NRIs.