Aiming at the problems of limited insulator type recognition, poor positioning accuracy and lack of robustness in existing insulator detection algorithms, a multi-type insulator detection algorithm based on improved YOLOv7-tiny is proposed. Firstly, the K-means++ algorithm is used to recluster the anchor box to obtain the anchor box size which is more suitable for multi-type insulator datasets. Secondly, the WIoUv3 loss function based on the dynamic non-monotone focusing mechanism is designed to address the imbalance between positive and negative samples in the training process. On the network structure, firstly, the Cross-stage Feature Fusion-ConvNeXt Block (CFFCB) is used to capture more context information at the Backbone, and some occluded insulators are accurately detected. At the same time, at the Neck, the SPPCSPF (Spatial Pyramid Pooling Cross Stage Partial-Fast) is proposed to replace the original SPPCSP, (Spatial Pyramid Pooling Cross Stage Partial), which effectively improves the detection success rate when the insulator is close to the background, and effectively improves the missed detection situation. After experimental testing, compared with YOLOv7-tiny, the mAP of the improved network model is increased by 2.1%, reaching 97.6%, which effectively improves the detection accuracy of various insulator types. Finally, the grabbing experiment is carried out on the UR5 manipulator by using the detection results of the improved algorithm. The actual grabbing success rate is about 90%, which verifies the feasibility of the algorithm.