The scalar neurons of convolutional neural networks cannot express the feature location information,and have poor adaptability to the complex vehicle driving environment,resulting in low traffic sign recognition rate. Therefore,an intelligent traffic sign recognition method based on capsule network was proposed. Firstly,the very deep convolutional neural network was used to improve the feature extraction part. Then,a pooling layer was introduced in the main capsule layer. Finally,the movement index average method was used for improving the dynamic routing algorithm. The test results on the GTSRB dataset show that the improved capsule network method improves the recognition accuracy in special scenes by 10. 02 percentage points. Compared with the traditional convolutional neural network,the proposed method has the recognition time for single image decreased by 2. 09 ms. Experimental results show that the improved capsule network method can meet the requirement of accurate and real-time traffic sign recognition.

When samples from different classes over classification boundary are seriously overlapped, cluster assumption may not well reflect the real data distribution, so that semi-supervised classification methods based cluster assumption may yield even worse performance than their supervised counterparts. For the above unsafe semi-supervised classification problem, an Adjusted Cluster Assumption and Pairwise Constraints Jointly based Semi-Supervised Support Vector Machine classification method (ACA-JPC-S3VM) was proposed. On the one hand, the distances of individual unlabeled instances to the distribution boundary were considered in learning, which alleviated the degradation of the algorithm performance in such cases to some extent. On the other hand, the information of pairwise constraints was introduced to the algorithm to make up for its insufficient use of supervision information. The experimental results on the UCI dataset show that the performance of ACA-JPC-S3VM method would never be lower than that of SVM (Support Vector Machine), and the average accuracy is 5 percentage points higher than that of SVM when the number of labeled samples is 10. The experimental results on the image classification dataset show that the semi-supervised classification methods such as TSVM (Transductive SVM) have different degrees of unsafety learning (similar or worse performance than SVM) while ACA-JPC-S3VM can learn safely. Therefore, ACA-JPC-S3VM has better safety and correctness.