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The water ingress defects of composite honeycomb structure produced in the service process of aircraft will seriously threaten flight safety.The detection of water ingress defects rely on manual work, low detection efficiency and low degree of automation in the daily maintenance and overhaul process. Aiming at this problem, considering the limited computing power of mobile or embedded devices used in actual maintenance scenarios, a module SE-IRthat integrates sequeeze and excitation block and inverted residual algorithm is designed, and a lightweight network SE-IR LCNN based on SE-IR module is further built. As much as possible to ensure the accuracy of network detection while reducing the number of network parameters. In order to verify the effectiveness of the proposed lightweight network, digital X-ray photography equipment is used to obtain digital images of honeycomb structures and their water defects and make data sets. The experimental results on this dataset show that the classification accuracy of the proposed lightweight network is 99.20 %, which can effectively screen out the water accumulation defects of aircraft honeycomb structure. Compared with the classical network ResNet-50 and VGG-16, the accuracy of the proposed network is increased by 9.6 % and 3.66 % respectively, and the number of parameters is only 1 / 10 of the number of parameters of ResNet-50 and 1 / 50 of the number of parameters of VGG-16.