Pin missing is a common bolt defect in transmission line. It is important for the safe operation of transmission line to detect pin-missing bolt in time. The bolt defect detection based on fully supervised detection model needs object-level labeling, which will consume much human and material resources. In order to reduce this consumption, a weakly supervised detection method for pin-missing bolt of transmission line based on improved PCL(Proposal Cluster Learning) model is proposed, which only uses image-level labeling to detect pin-missing bolt. The channel attention mechanism was introduced to generate weighted feature map, highlight the features of target area, and effectively mine the bolt position information. The weighted cross entropy loss function was used to control the contribution of positive and negative samples to the loss value, increase the loss proportion of difficult samples, and improve the attention and recognition ability of the model to the bolt target. The fully supervised multi-task learning idea was integrated, so that the model could revise the pre-obtained bounding box with the increasement of iteration. The experimental results show that on the test set, compared with the basic model, the AP(Average Precision) value of pin-missing bolt of the improved model is increased by 25.6% and the mAP(mean Average Precision) value of is increased by 25.4% , which verifies the robustness of the proposed method in the paper.