Aiming at the problem of low detection accuracy of multiple types of container damage targets at different scales in the complex port environment,a port container damage detection algorithm based on improved YOLOv5 is proposed.By using one-dimensional convolution to improve the pooling operation of spatial attention module in CBAM attention mechanism,the residual structure was designed,and the influence of introducing improved CBAM blocks in different positions on the model performance was verified,to explore the best scheme and fusion location to minimize the influence of complex background on detection results.In order to solve the problem of significant change in feature transformation of different container damage images,the neck feature fusion network was improved based on the Bi-FPN network structure,it can enhance the feature fusion ability of the network to multi-scale targets without increasing the amount of calculation too much.Finally,EIOU Loss was used to replace GIOU Loss as the loss function of the algorithm,which reduced the regression loss of the bounding box and improved the detection accuracy of the algorithm. The experimental results show that the average detection accuracy of the improved YOLOv5 model have reached 98.32%,which is 4.28%higher than the original YOLOv5 algorithm.At the same time,it ensures the detection speed and verifies the validity of the algorithm proposed in this paper,which is of great significance to the industrial deployment of high-precision container inspection in port enterprises.