The hot-rolled steel strip surface defects detection methods have low detection accuracy and suffer background clutters.To solve the issues mentioned above,this paper proposes a CA-YOLOv5 defects detection method based on coordinate attention (CA).The improvement of CA-YOLOv5 are three aspects.First,to make the YOLOv5 more suitable for strip defects detection,the data argumentation using the 4 or 9 mosaic images randomly and hyperparameter optimization based on the genetic algorithm (GA) are employed in the input of the network.Second,the CA mechanism is introduced between the backbone and the neck model to improve the feature extracting ability of the network.Third,each detecting head is decoupled to separate the classification and regression tasks,which could further boost the accuracy of defects detection.Comparative experiments on NEU-DET dataset show that the proposed network obtains 84.36% mean average precision (mAP),which not only exceeds YOLOv5 by 6.68%,but also outperformances other state-of-the-art detectors.