## 1 Introduction  
Object detection is a research topic in digital image processing, which mainly includes two sub-tasks of object positioning and object classification. It is widely used in intelligence monitoring [1], autonomous driving [2], industrial detection [3, 4], remote sensing image analysis [5], aerospace detection [6], and other fields. Nowadays, with the rapid rise of deep learning, new research ideas and directions have been brought to object detection. Due to the variability, complexity, and morphological heterogeneity of object detection, problems such as the detection accuracy of small objects are low [7, 8, 9]. To improve the accuracy of small object detection, we propose a method based on a multi-scale feature fusion and attention mechanism module. At present, deep learning-based object detection algorithms are developing rapidly, which can be roughly divided into two-stage object detection algorithms and single-stage object detection algorithms. The core of the two-stage algorithm is to replace the artificial feature extraction method with a convolutional neural network. [PERSON] et al. proposed a deep learning-based object detector R-CNN (Region Convolutional Neural Network) [10]. However, due to a large amount of calculation of the model, the detection efficiency is low. [PERSON] et al. proposed the SPP-Net (Spatial Pyramid Pooling Network) [11] algorithm. [PERSON] et al. proposed the Fast R-CNN [12] to introduce the mapping relationship between the image and the feature layer. [PERSON] et al. proposed the Faster R-CNN [13]. Based on Fast R-CNN, RPN (Region Proposal Network) was added, which can improve the network calculation speed. Since the two-stage detection method needs to extract candidate regions, and then make secondary corrections based on the candidate regions to obtain the detection results, the feature load of each layer is too large. It is difficult to achieve real-time detection and high speed. To solve the speed problem of the two-stage object detector, [PERSON] et al. proposed YOLO (You Only Look Once) [14], but the positioning and classification accuracy of the algorithm was relatively low. [PERSON] et al. proposed the SSD (Single Shot MultiBox Detector) [15], which largely improves the detection speed and detection precision of the single-stage object detector. [PERSON] et al. proposed the DSSD (Deconvolutional Single Shot Detector) [16], which used the Residual-101 Network [17] as the backbone with a stronger feature extraction capability. [PERSON] et al. proposed the FSSD (Feature Fusion SSD) [18]. [PERSON] and [PERSON] proposed YOLOv2 [19], which improves YOLO's speed and precision. [PERSON] and [PERSON] proposed YOLOv3 [20], which used a newly designed Darknet-53 residual network combined with FPN (Feature Pyramid Network) [21] for multi-scale feature maps fusion, and the performance of the object detector was further improved, but the accuracy of identifying the object position is poor, and the use of a fixed bounding box in each grid reduces the number of candidate frames. Given the deficiencies of the above models, we propose an object detection method based on a multi-scale feature fusion and attention mechanism module. Our main contributions are as follows:  
(1) Based on the SSD model, we deconvolute and amplify the deep feature layer, then fuse it with the shallow feature layer, fully combining the detailed information in the shallow network and the semantic information in the deep network to improve the accuracy of small object detection. (2) We add a channel attention mechanism to the network, and introduce a GC (Global Context) block as an attention module between different scales of the network, so that the network has the ability of global context modeling and the advantage of remote dependency capture, retains more object's features information, suppress irrelevant other information and improve the ability of the network to extract features, thereby enhance the detection accuracy.