Existing high precision object detection algorithms mostly rely on super deep backbone networks, such as ResNet and Inception, making it difficult to meet real-time detection requirements. On the contrary, some lightweight backbone networks, such as VGG-16 and MobileNet, fulfill real-time processing but their accuracies are often criticized, especially when the targets are small. In this study, we explore an alternative to build a fast and accurate detector by strengthening the feature extraction ability of lightweight backbone networks, using a new receptive field block based on a single shot multi-box detector (SSD). Simultaneously, to make full use of the semantic information extracted from deep networks, a feature fusion module is designed and added, thereby improving the overall accuracy and enhancing the detection effect of the model for small targets, while still achieving real-time detection. To further verify the validity of introducing new modules, we have tested our model on the PASCAL VOC2007 data set and achieved an accuracy of 80.5% which is 3.3 percentage points higher than that of the original SSD model. In addition, the detection speed of the proposed model reaches 75 frame/s, and its overall performance is better than that of most of the current models.