Abstract: Pins, the important components in the transmission lines, functions to fix the nuts. Once a pin defect occurs, a power failure in a large area may be caused. The tiny size of a pin makes the pin defect detection a challenging task. Therefore, we improve the Feature Pyramid Network and propose a better network structure specifying for pin defect detection -- PinNet. First, the feature extraction layer is replaced by SCNet, the latest variant of the residual network, to extract more discriminative features. Second, on the basis of FPN, PinFPN is designed to further enhance the underlying semantic information and location information, and also to improve the ability to detect small objects. Finally, to solve the problem of lack of dataset, various methods of data augmentation are adopted to expand the training samples and improve the robustness of the model. On the test set, compared with the original algorithm, the AP value of our model on the pin missing target is improved by 4.2%, which also has a great advantage compared with other mainstream algorithms. In order to further prove the effectiveness of the algorithm, the model has been tested on the small target defect data sets of other transmission lines, which also achieves good results.