In the three-dimensional (3D) precision measurement of large component, the detection accuracy of cooperative targets is low due to complex structure of large components and various measurement environment. To solve this problem, a multi-type cooperative target detection method using improved YOLOv2 convolutional neural network was proposed. Firstly, the data augmentation method combined with WGAN-GP was employed to amplify the number of cooperative target images. Secondly, the convolutional layer dense connection was used instead of the YOLOv2 basic network layer-by-layer connection to enhance image feature information flow, and the spatial pyramid pooled was introduced to convergence image local area feature. Base on those two parts, the multi-type cooperative targets detection method with improved YOLOv2 convolutional neural network was constructed. Finally, the multi-type cooperative targets detection model with improved YOLOv2 convolutional neural network was trained by the augmentation dataset for detecting the multi-type cooperative targets. The experimental results of multi-type cooperative target detection indicate that, detection precision of the proposed method is up to 90.48%, and detection speed is 58.7 frame per second by using image dataset of multi-type cooperative targets to test. This method has higher precision, rapid speed and strong robustness, which can satisfy the multi-type cooperation targets′ detection requirements for 3D precision measurement of the large component.