## 2 Related Work  
### Small Target Detection  
**Multi-scale feature fusion**. The Single Shot MultiBox Detector (SSD) [11] uses a multi-scale feature map for detection, using a large feature map to detect small targets and a small feature map to detect large targets. SSD detects the feature map obtained from each convolution and completes the target location and classification at one time. However, it detects the target by convolution of the feature map, unlike the full connection layer of YOLOv1 [21], so it will lose a lot of spatial information. In addition, each point on these feature layers constructs six prior frames at different scales. Finally, the output obtained on all feature maps is combined, and the detection results are obtained through NMS (non-maximum suppression). MDSSD [22] adds high-level features with rich semantic information to low-level features through deconvolution Fusion Block. In particular, several high-level features with different proportions were up-sampled at the same time, and then the connection was skipped to form a more descriptive feature map of small objects. Finally, these new fusion features were predicted. Different from the element summation strategy adopted by MDSSD [22], the convolution neural network based on the deconvolution region (DR-CNN [23]) adopted the concatenation strategy to fuse a multi-scale feature map for small traffic symbol detection. The channel-aware deconvolution network (CADNet) [24] was proposed to study the relationship between the deep feature maps in different channels to avoid the simple superposition of feature maps. By using the correlation between different scale features, the recall rate of small objects can be improved at a lower computational cost.  
**Context-based detection method**. The enhanced R-CNN [5] proposed in the context network can be considered the first detector focusing on small target detection. In this work, a new regional proposal network (RPN) was proposed to encode the context information of small object proposals. Internal and external networks (ION) [25] used spatial recurrent neural networks (RNNs) to search the context information outside the target area; the skip pooling method was then used to obtain internal multi-level feature mapping. [PERSON] et al. [26] integrated the U-V parallax algorithm with the faster R-CNN, which combined internal and contextual information. The new framework of FASSD [15] started from the baseline SSD and then proposed three components to improve the detection accuracy of small targets. First, SSDs and features were fused to obtain context information, named F-SSD. Second, SSDs with reserved modules enabled the network to focus on important components, called A-SSDs. Third, the researcher combined feature fusion and attention modules, called FA-SSD.  
**Attention Mechanisms.** Inspired by the human visual system, the attention mechanism has been introduced into convolutional neural networks in recent years to improve the performance of target detection [27; 28]. According to the form of attention acting on the feature map, the attention mechanism was mainly divided into channel attention [29], spatial attention [30], and channel and spatial mixed attention [31]. The SE attention mechanism [29] is a popular attention mechanism at present. It uses two-dimensional global pooling to calculate the channel attention mechanism, but the channel attention mechanism only pays attention to the coding of channel information, ignoring the influence of location information [32], and location information is very critical in visual tasks, so enhancing the extraction of location features is a key technology to improve the detection accuracy.