The existing scene text detection methods based on deep learning generally use a deep neural network as the backbone network for feature extraction. Although it can achieve a striking detection effect,the entire detection model is very large which results in poor detection efficiency. If the large backbone network is replaced by a small backbone network directly, it will often fail to extract enough semantic features and can't achieve an ideal detection result. To reduce the size of the scene text detection model and promote the detection efficiency, a dual-path feature fusion based scene text detection(DPFFSTD) is proposed. Based on a relatively lightweight basic network EfficientNet-b3, the DPFF uses two branches for feature fusion to detect scene text. One branch uses a feature pyramid network to fuse the features with different levels. The other branch uses an atrous spatial pyramid pooling to enlarge receptive field and obtains the features of different scales. And then the features from the above two branches are fused to form more features only with a very small increasing computation, which makes up for the shortage of features caused by the small backbone network. The experimental results on three benchmark datasets show that the proposed method significantly reduces the number of the model parameters and greatly improves the detection efficiency while maintaining a high detection effect.