# ADSSD: Improved Single-Shot Detector with Attention Mechanism and Dilated Convolution
###### Abstract  
The detection of small objects is easily affected by background information, and a lack of context information makes detection difficult. Therefore, small object detection has become an extremely challenging task. Based on the above problems, we proposed a Single-Shot MultiBox Detector with an attention mechanism and dilated convolution (ADSSD). In the attention module, we strengthened the connection between information in space and channels while using cross-layer connections to accelerate training. In the multi-branch dilated convolution module, we combined three expansion convolutions with different dilated ratios to obtain multi-scale context information and used hierarchical feature fusion to reduce the gridding effect. The results show that on PASCAL VOC2007 and VOC2012 datasets, our 300 \(\times\) 300 input ADSSD model reaches 78.4% mAP and 76.1% mAP. The results outperform those of SSD and other advanced detectors; the effect of some small object detection is significantly improved. Moreover, the performance of the ADSSD in object detection affected by factors such as dense occlusion is better than that of the traditional SSD.  
SSD; small object detection; attention mechanism; multi-branch dilated convolution +
Footnote †: journal:  
Unlike YOLO, the SSD performs better because it outputs and predicts at different scales. The SSD takes VGG-16 as the backbone network and uses pyramid structures to obtain multi-scale feature information for location and classification. The deeper network layers are used to detect larger objects, and smaller objects are detected by shallow network layers, so that the SSD will perform better in the detection of small objects. However, background information still affects the extraction of small object feature information. We use the attention mechanism to strengthen the connection between the channel and the spatial feature information; reduce the weight of the background information; achieve the purpose of suppressing background information and focusing on key information; and then improve the extraction ability of feature information. At the same time, since only shallow features are downsampled, resulting in a lack of context information to assist small object detection, we use convolution modules with different expansion ratios to capture local and global context information. In addition, we use hierarchical feature fusion to solve the problem of local information loss and information obtained from a distance being not relevant due to dilated convolution.  
To address these issues, this paper proposed an algorithm ADSSD based on an improved SSD.  
1. To suppress background information and improve the ability to extract small object feature information, we propose a module that is improved based on a CBAM, called a Residual Convolutional Block Attention Module (RCBAM).
2. We constructed a multi-branch dilated convolution to extract contextual information, introduced hierarchical feature fusion (HFF) for de-gridding, and used cross-layer connections to improve learning efficiency and prevent degradation.
3. We have improved the detection speed while improving the detection accuracy, which is not available in other algorithms.  
In Section 2, we cover the work involved in the module. In Section 3, we introduce the network structure of the ADSSD and the structure of the module used in the improvement. In Section 4, YOLO, the SSD, and other detectors are compared with the ADSSD, and the experimental results are analyzed. In Section 5, the conclusion and prospect of the future are provided.