An improved YOLOv4-tiny multi-target detection algorithm is proposed to solve the problem of multi-target detection on shipboard. A convolutional block attention module (CBAM) is introduced into the convolutional neural network to focus on shipboard targets and suppress background features by mixing channel features and spatial features, so as to improve the anti-background interference ability of the network; an spatial pyramid pooling (SPP) structure is added according to the change of target scale to fuse features of different scales, so as to improve the detection ability of targets of different sizes; Mish activation function is used instead of Leaky ReLU activation function for better generalization ability. The experimental results show that the average detection accuracy of five kinds of shipboard targets is 92.22%, which is close to the 96.48% of YOLOv4 algorithm, and the detection speed frames per second (FPS) reaches 42.5 frame/s, which is much higher than the 18 frame/s of YOLOv4 algorithm. The algorithm balances the relationship between accuracy and speed, and can detect the target on the warship in real time.