# Object Detection Algorithm Based on Multiheaded Attention
## 2 Related Studies  
Prior to the advent of CNNs, early target detection methods were usually based on sliding windows, such as Support Vector Machines with a Histogram of Oriented Gradients (HOG-SVM) [9], or Deformable Part Models (DPMs) [10]. Most of them have been extensively used to classify Regions-Of-Interest (ROIs) into various categories. DPM [10] is one of the most commonly used methods. The model was proo-stage detecposed by [PERSON] et al. in 2008 [10]. As its name suggests, it is a component-based detection algorithm. It won the PASCAL VOC target detection championship three times. The algorithm is based on the extraction of the artificial features of DPM followed by the use of latent SVM classification. This feature extraction method has obvious limitations. Firstly, the DPM feature is computationally complex and the calculation speed is low. Secondly, the artificial feature yields poor detection outcomes on objects with rotation, stretching, and viewing angle changes. These drawbacks largely limit the application scenarios of the algorithm. To solve this problem, [PERSON] et al. proposed a target detection method based on a deep R-CNN [4] that exceeded the performances of the previous methods. Ever since the introduction of this method, almost all of the optimal target detection methods have been based on the CNN. Two-stage detectors and single-stage detectors are currently the two mainstream target detection methods. Two-stage detector examples include the Spatial Pyramid Pooling (SPPNet) [11], Fast R-CNN [12], Faster R-CNN [3], and the R-FCN [5]. Most of them use the top layer of the convolutional network to detect objects of different sizes. These methods first use a separate generator to generate a set of candidate objects, such as Selective Searches [13], Edge Boxes [14], and Region Proposal Network (RPN). Most of them are either based on super-pixel merging (such as the Constrained Parametric Min-Cuts (CPMC) [15]), or on sliding window algorithms (such as the Edge Boxes [14]). Their common feature is the CNN which is mainly used as a classifier. Object boundaries are not predicted. While these methods greatly improve the detection accuracy, they are computationally intensive and slow. Single-stage detectors (such as YOLO [1] and SSD [2]) have drawn attention because of the shortcomings of the slower two-stage detectors. These detectors replace the region proposal stage by covering the entire image with a fixed set of anchors of different sizes. Small objects are detected in shallow convolution layers with high-resolution features, while large objects are detected in deep convolution layers with low-resolution features. Therefore, the SSD can extract rich features with smaller input sizes to reduce the computational cost compared to the Faster R-CNN [3]. However, this method type does not pay attention to the local information of each location, and the information in the lower convolution layer is not fully utilized. Therefore, although the single-stage method is less expensive in reference to the calculation compared to the two-stage method, the accuracy is still inadequate. To improve the performance of two- and single-stage detectors, the academic community has proposed various strategies to aggregate multiscale information into target detectors. Multiscale CNN (MS-CNN) [16] utilizes feature maps at various resolutions to detect objects at different scales, thus resulting in a set of variable receptive field sizes which are used to cover many object sizes. Rainbow-SSD (R-SSD) [17] combines the characteristics of different layers by pooling and deconvolution that not only strengthens the relationship between different layer feature maps but also increases the number of these different layer feature maps. The Feature Fusion Single Shot Multibox Detector (FSSD) [18] takes each feature concat level and generates a feature pyramid from it. Enhanced SSD (ESSD) [19] fuses feature maps of different output layers and proposes a visual reasoning method to enhance the network.