To address the problems of diverse and complex shapes of steel surface defects， detection target missing， and large number of algorithm parameters， a lightweight VTG-YOLOv7-tiny steel defect detection algorithm was proposed. The method first designed VoVGA-FPN network to reduce the loss of information during information transmission and enhance the network feature fusion ability； second， it constructed a triple coordinate attention mechanism to improve the model's feature extraction ability of spatial and channel information； third， it introduceed ghost shuffle convolution to reduce the model parameters and computation while improving the accuracy； fourth， it added a large target detection layer to improve the problem that some defects in the feature map occupy a large proportion， resulting in low detection accuracy. The improved algorithm was verified on the NEU-DET and Severstal steel defect datasets. Compared with the original model， the mAP of the improved algorithm is increased by 5.7% and 8.5%， respectively； the parameters and computation are reduced by 0.61 M and 4.2 G， respectively； the accuracy and recall are increased by 7.1%， 1.8% and 8.9%， 7.0%， respectively. The experimental results show that the improved algorithm better balances the detection accuracy and lightweight， and provides a reference for edge terminal devices.