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In view of the high complexity of blind road scene when blind people travel, the existing target detection algorithm is difficult to extract the features of long-distance obstacles and strip obstacles, resulting in missed detection and other problems. Asymmetric convolution module (ACB) was added for bar obstacle detection to strengthen feature extraction in vertical and horizontal directions. A hybrid pooling module was constructed. Strip pooling was introduced into the network and pyramidal pooling was integrated into a hybrid pooling module (MPM) to enhance the detection effect of the network on the long and non-long obstacles. At the end of the network, the fusion mode of features is changed, and the multiplication form of low-level features and advanced features is used to strengthen blind obstacle recognition in complex scenes. The experimental results show that, compared with YOLO V4, the improved algorithm has improved in multiple evaluation indexes in the blind obstacle data set. In the actual scene test, the detection accuracy of long-distance obstacles and strip obstacles is improved obviously.