To address the problem of low target detection accuracy in lightweight networks, a lightweight target detection network MobileNet-RFB-ECA based on MobileNet is proposed. To consider the multi-scale characteristics of the target, this study proposes a feature pyramid network structure based on the lightweight extended receptive field block (RFB), which enhances the adaptability of the network to the multi-scale characteristics of the target. Moreover, owing to the large computation caused by the complex attention module, an efficient channel attention (ECA) module is added to the backbone feature extraction network to improve the performance of the convolutional neural network. Experiments reveal that compared with conventional MobileNet, the proposed method improves the detection accuracy by 4.2 percentage points and 15.4 percentage points on the PASCAL VOC and KITTI datasets, respectively. In addition, the model sizes of the proposed method are 50.3 and 48.5 MB for the aforementioned datasets, respectively, and the average detection speed achieved is 34 frame/s.