## 1 Introduction  
As one of the fundamental visual recognition problems of computer vision, object detection is the basis of many other computer vision tasks, such as instance segmentation [1, 2] and object tracking [3]. Object detection must identify not only the object's categories but also locate each item. Object detection has been extensively studied in the literature. In recent years, benefited by the rapid development of Deep Convolutional Neural Networks (DCNN), object detectors based on deep learning have achieved significant breakthroughs. Object detection has been widely used in the real world, such as robot vision, video surveillance, and autonomous driving. In order to improve the detection accuracy, most research focuses on the design of increasingly complex object detectors such as R-CNN [1], Single-Shot MultiBox Detector (SSD) [4], You Only Look Once (YOLO) [5], and their variants [6, 7, 8, 2, 9]. Although they have achieved high detection accuracies, such object detection networks are usually challenging to handle for embedded devices due to computational and memory limitations. Therefore, the design and development of more efficient deep neural networks for real-time embedded object detection are highly expected. Current state-of-the-art object detectors with deep learning can be mainly divided into two major categories: two-stage detectors [1, 2, 6, 7] and one-stage detectors [4, 5]. Two-stage detectors firstly generate a set of preselected boxes for the object to be detected and then perform object category prediction. Therefore, Two-stage detectors often reported state-of-the-art results on many public benchmark datasets. However, they are relatively slow. One-stage detectors directly train convolutional neural network models to map image pixels to the coordinates of the bounding box, so they are much faster and more desired for real-time object detection applications. Despite the fact that one-stage detectors have achieved good results in the trade-off between accuracy and speed, it is not realistic to port them directly to embedded applications because the network models are often large. Recently, research on lightweight object detection networks that can be applied to embedded devices has attracted more and more researchers. Different artificially designed lightweight neural network architectures have been utilized for object detection, such as the lightweight network architecture MobileNet proposed by Google [10; 11], which uses deep separable convolution instead of standard convolution. ShuffleNet [12] is proposed through the point-by-point group convolution kernel channel shuffling technology by Face++. The Fire module introduced by [PERSON] et al. in SqueezeNet [13] consists of two parts, a compression layer (squeeze) and an expansion layer (expend), which reduces the amount of calculation required for the entire model by reducing the number of channels in the squeeze layer. The AF-SSD [14] applied MobileNetV2 [11] and extra convolutional layers with basic units in ShuffleNetV2 [15] and depthwise separable convolutions as the lightweight backbone. Experimental results show that AF-SSD is a fast and accurate detector with few parameters. Many other studies [16; 17; 18; 19; 20; 21; 22; 23; 24] have shown that object detectors using these lightweight networks as the backbone achieved state-of-the-art results. However, these lightweight networks need to be pre-trained on universal datasets such as ImageNet before using them as backbone networks for object detection. Pre-training is usually performed on the datasets of general image classification tasks, so it is difficult to transplant them to specific application scenarios like medical image detection. Simultaneously, these pre-trained network models have a large number of parameters and fixed structures, which makes it difficult to optimize the structure.