Steel is one kind of the important raw materials in China's industrial production.The surface quality problems of steel will directly affect the use of the products,which will bring unpredictable risks.Therefore,it is significance to carry out defect detection on the surface of steel.In the defect detection process,some defect features may not be obvious,resulting in inaccurate defect localization and high detection difficulty.An improved Faster RCNN algorithm was proposed in this study,which would introduce an adaptive module on the backbone feature extraction network,enhance the ability of the network to extract more effective features.DBSCAN clustering algorithm was used to obtain a suitable anchor frame,which greatly improved the detection efficiency of the algorithm.The experimental results showed that the proposed Faster RCNN model can achieve a substantial improvement in the detection of obscure defect features.Compared with other detection algorithms,the proposed algorithm can aobtain accurate defect localization and a high classification success rate in the detection of defects on the surface of steel plate.
NETL
NSTL
万方数据
