Due to the large number of pictures of electric power fittings taken in the power inspection, the inspection workload is large. In order to improve the automatic detection effect of electric power fittings, this paper proposes a UNet-DB_ECA (UNet Dimensionality Reduction, BN, and ECANet, UNet-DB_ECA) network detection method based on UNet network. First reduce the width of the UNet network, then embed the efficient channel attention mechanism module ECANet (Efficient Channel Attention Networks, ECANet) and Batch Normalization (BN) in the network, and finally introduce the Hard-Swish activation function, thus constructing UNet- DB_ECA network. This paper uses the electric power fittings detection dataset to conduct experiments. The experimental results show that the method proposed in this paper has a good detection effect. Compared with the UNet network detection effect, it improves the detection effect and also takes into account the algorithm performance. In addition, the power fittings detection dataset contains seven types of fittings with different shapes, which shows that the method proposed in this paper has good generalization ability, so the method has certain application prospects in the automatic detection of power fittings.