Abstract:Pipelines are welded by steel plates and their inner surfaces may have scratches, internal fractures, pits, and other problems. If the abnormality on the inner surfaces cannot be found in time, a large number of unqualified products will be produced and the enterprises will have losses. This study designs a method for detecting anomalies on the inner surfaces of steel pipes based on image saliency. First, the image information after discrete cosine transform is collected and then fused with the phase spectrum of the image to obtain the final saliency map. Finally, the detection results are mapped to the original image through the connected region detection. Experimental results show that this method has a more remarkable detection effect, higher accuracy, and better stability and practicability than its counter parts.