Steel coil shape defect detection and recognition method based on target detection

Abstract

The invention belongs to the field of steel coil rolling, and relates to a steel coil shape defect detection and recognition method based on target detection. A large number of steel coil shape pictures are obtained from the site to construct a coil shape defect data set, and the task of detecting and identifying the coil shape defects of the steel coil is completed in the traditional steel production industry by utilizing the currently advanced Faster-RCNN target detection algorithm with high identification precision and high detection speed, and meanwhile, the Faster-RCNN is subjected to model compression by pruning, so that the model can meet the industrial embedded requirements. According to the method, a modern intelligent detection technology is utilized, and the method is applied toindustrial production detection of the steel coil.

Description

technical field [0001] The present invention relates to the field of iron and steel rolling, and more specifically, relates to a pruning optimization target detection algorithm Faster-RCNN realized by deep neural network, based on target detection, a unified method for coil shape defect detection and identification of target steel coils . Background technique [0002] Cold and hot rolled strip steel is an important basic material for economic development, and is widely used in various industrial fields such as machinery, construction, national defense, and aerospace. Among them, the coiling area is the last step of the hot rolling main rolling line, and the coil shape is used as the key index. The quality of the coil shape has a significant impact on transportation and subsequent production. As far as hot rolling itself is concerned, poor coil shape needs to be dealt with by downstream teams or recoiled on the upper leveling line, which will not only increase production cos...

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