As an open source object detection network, YOLOv3 has obvious advantages in speed and accuracy compared with the object detection network of the same period. Because YOLOv3 adopts a new type of full convolutional network (FCN), feature pyramid network (FPN), and residual network (ResNet), it requires high hardware configuration, leading to high development cost, which is not conducive to the popularization of industrial applications. Therefore, YOLOv3tiny is generally used for detection on embedded platforms. Although the calculation amount is small, the detection effect is far less than YOLOv3. To solve the problem of low detection speed of YOLOv3 on embedded platforms, a simplified version of the network based on YOLOv3 is proposed. Unlike YOLOv3, FCN, FPN, and ResNet, which are helpful for feature extraction, are retained as much as possible. the number of parameters and residual years of each layer is recued, and attempts are made to join densely connected networks and spatial pyramid pooling. Experimental results show that the number of parameters and detection speed of this network is much better than YOLOv3, and the mean average precision is a significant improvement compared to YOLOv3tiny in terms of in the PASCAL VOC2007 and 2012 datasets.