Aiming at the problems of poor spatial recognition and excessive training sample requirements of traditional convolutional neural networks used in power line patrol foreign object detection, an improved capsule network model is proposed. The gray-scale data and three-dimensional block matching filter algorithm are used to preprocess the line survey data set. The adaptive contribution pooling is proposed to reduce the amount of data information loss. The foreign object data depth information extraction unit extracts the main features to filter out redundant information, reduce the number of data to improve model performance, improve the foreign object recognition of main capsule layer and dynamic routing structure to adapt to power line patrol for the second classification of line foreign object detection. For adaptive contribution pooling and maximum pooling, non-pooling, traditional structure capsule network and improved capsule network, improved capsule network and AlexNet, GoogLeNet were respectively compared with foreign object recognition experiment and improved capsule network spatial recognition performance. The experimental results show that under 3 700 small training samples, after 20 trainings, the average accuracy of the improved contribution network of adaptive contribution pooling is greater than the maximum pooling by 2. 7%, and the improved capsule network is better than the non-pooling, traditional structure capsules. The average accuracy of the network is increased by 3. 6%, and the improved accuracy of the improved capsule network is 21. 9% and 12. 6% higher than that of AlexNet and GoogLeNet, respectively, and the improved capsule network still has an average accuracy higher than 91% in test data of different sizes and angles. The improved capsule network has high foreign object recognition ability under the condition of complicated spatial recognition and few training samples, and realizes high-efficiency and high-accuracy automatic unmanned line inspection foreign object detection.