Aiming at the current problems of low efficiency and poor detection accuracy of steel surface defects, a model, named ECC-YOLO, is proposed for steel surface defects detection based on YOLOv7. Firstly, in order to improve the capability of feature map information characterization of the backbone network, a feature enhancement module ConvNeXt is introduced, which enhances the feature extraction capability of the model for fine cracks by fusing the depth separable convolution and the large kernel convolution, secondly, a C2fFB module is designed, which enhances the capability of extracting the feature information of the target and at the same time, reduces the computational volume and parameter complexity of the model significantly. Finally, the MPCE module is designed with the help of the ECA attention mechanism to weaken the interference of the complex background information on the steel surface defect detection and improve the detection efficiency. Finally, extensive experimental results show that the mAP of the model of ECC-YOLO reaches 77.2% on the NEU-DET dataset, and compared with YOLOv7, the detection accuracy of ECC-YOLO is improved by 10.1%, and the number of model parameters is reduced by 9.3%, which gives the model a better comprehensive performance in steel surface defect detection.