# Object Detection Algorithm Based on Multiheaded Attention
## 1 Introduction  
Target detection is a fundamental, challenging, and long-standing problem, and has been a hotspot in the field of computer vision research for decades [1; 2; 3]. The purpose of target detection is to determine if any instances of a specified category exist in a given image. If there is an object to be detected in a specific image, target detection returns the spatial position and the spatial extent of the instances of the objects (based on the use a bounding box, for example). As one of the cornerstones of image understanding and computer vision, target detection forms the basis for more complex or higher-level visual tasks, such as object tracking, image capture, instance segmentation, and others. In addition, target detection is also extensively used in areas such as artificial intelligence and information technology, including machine vision, automatic driving, and human-computer interaction.  
Recently, the method of automatic learning of represented features from data based on deep learning has effectively improved the performance of target detection. Neural networks are the foundation of deep learning. Therefore, the design of better neural networks has become a key issue toward the improvement of target detection performance. Recently developed target detectors that have been based on convolutional neural networks (CNNs) have been classified in two types: The first is the two-stage detector type, such as Region-Based CNNs (R-CNNs) [4], Region-Based Full Convolutional Networks (R-FCNs) [5], and Feature Pyramid Networks (FPNs) [6], and the other is the single-stage detector, such as the You Only Look Once (YOLO) [1], Single-shot detector (SSD) [2],and the RetinaNet [7]. The former type generates a series of candidate frames as samples, and then classifies the samples based on a CNN; the latter type does not generate candidate frames but directly converts the target frame positioning problem into a regression processing problem.  
To maintain real-time speeds without sacrificing precision in the various target detectors described above, [PERSON] et al. [2] proposed the SSD which is faster than YOLO and has a comparable accuracy to that of the most advanced region-based target detectors. SSD combines the regression idea of YOLO with the anchor box mechanism of a Faster R-CNN [3], predicts the object region on the feature maps of the different convolution layers, and outputs discretized multiscale and multi proportional default box coordinates. The convolution kernel predicts the frame coordinate compensation of a series of candidate frames and the confidence of each category. The local feature maps of the multiscale area are used to obtain results for each position of the entire image. This maintains the fast characteristics of the YOLO algorithm and also ensures that the frame positioning effect is similar to that induced by the Faster R-CNN [3]. However, SSD directly and independently uses two layers of the backbone (VGG16) and four extra layers obtained by a convolution with stride 2 to construct the feature pyramid but lacks strong contextual connections.  
To solve these problems, we propose in this study a new, single-stage detection architecture, commonly referred to as MANet, which aggregates feature information at different scales. Our MANet achievs 82.7% mAP on the PASCAL VOC 2007 test [8].  
The contributions of our work can be summarized as follows:  
1. We propose a framework referred to as MANet that combines feature information at different scales for better performance
2. To fuse feature information at different scales, we propose a new attention mechanism referred to as the fusion attention
3. To fuse multiple scale feature information in a more efficient manner, we designed different multihead fusion modules to generate more efficient feature representations