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Steel Defect Detection Based on Position-sensitive Convolution and Attention Mechanisms
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    Abstract:

    To improve the accuracy of steel defect detection, a defect detection algorithm YOLOv5s-FNCE based on YOLOv5s is proposed. Firstly, a novel NAMAttention attention mechanism is added to the backbone feature extraction network to improve the perception and differentiation of the target; and a new C3-Faster is proposed to extract the features; the positional convolutional CoordConvs is introduced in the feature fusion network and at the output to enhance the semantic perception ability and global perception ability of the target; and finally, a new loss function Focal-EIoU is introduced to accelerate the convergence speed and improve the regression accuracy. Experimental results show that the mean average accuracy of the YOLOv5s-FNCE algorithm on the steel surface defects dataset reaches 75.1%, which is 1.7% higher than that of the original YOLOv5s, the detection speed is increased by 20.5%, which proves that the algorithm can effectively improve the detection speed and accuracy in steel defect detection.

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  • Online: April 28,2025