关键词: 钢材表面缺陷; , 神经网络; , 小目标检测; , 特征融合

Abstract: Aiming at the problem of poor performance of traditional Faster R-CNN(Region-Convolutional Neural Networks) in detecting small target defects on steel surface, a new method for steel surface defect detection based on improved Faster R-CNN is proposed. First, the GA-RPN(Guided Anchoring Region Proposal Network) is introduced to predict the position and shape of the anchor points, and an adjustable mechanism is designed to solve the problem that the shape offset of network anchors exceed the region of interest, thereby solving the influence of irrelevant features. Then, a multi-task FPN (Feature Pyramid Network) structure is used to shorten the high-level feature location information mapping path, and can solve the insufficient features fusion of adjacent layers features fusion and re-sampling, and to improve the performance of small target detection. The results show that the recall rate and accuracy of the network are improved. Therefore, this method has better performance and can effectively detect steel surface defects.

Key words: surface defects of steel, neural network, small target detection, feature fusion