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.