Based on depth separable convolution, a small object detection network for embedded platform, MTYOLO (MobileNet Tiny-Yolo), is proposed. It divides the image into many grids and replaces the traditional convolution by the depth separable convolution, which decreases the number of parameters and computational cost. The point convolution and the feature map merging are adopted to improve the detection accuracy. The experimental results show that the size of the proposed MTYOLO network model is 41 MB, approximately 67% of that of Tiny-Yolo model. Furthermore, its detection accuracy on the PASCAL VOC 2007 dataset is up to 57.25%, superior to the Tiny-Yolo model’s. The proposed model is particularly suitable for application in embedded platforms.