## 2 Disadvantage of Classical Object Detection Methods Based on Regression  
Currently, the classical object detection methods based on regression are YOLO and SSD. YOLO is a single neural network that can perform object region detection and object classification simultaneously. Unlike early object detection methods based on region proposals, YOLO achieves end-to-end object detection without dividing the detection process into several stages. SSD is a single shot multibox detector that integrates the regression approach of YOLO with the anchor mechanism of Faster R-CNN [20]. On the one hand, the regression approach can reduce the computational complexity of a neural network to improve the real-time performance. On the other hand, the anchor mechanism is helpful for extracting features at different scales and different aspect ratios. Moreover, the local feature extraction method of SSD is more reasonable and effective than the general feature extraction method of YOLO with regard to object recognition. Furthermore, because the feature representations corresponding to different scales are different, a multi-scale feature extraction method [26] has been applied in SSD, thereby enhancing the robustness of object detection at different scales. Because YOLO divides each image into a fixed grid, the number of detected objects will be limited. For example, consider a grid scale of S \(\times\) S, where S = 7. Because each grid yields 2 predicted bounding boxes, only 98 predicted bounding boxes are observed in a single detection, which means that no more than 98 objects can be detected at one time. Moreover, because one grid cell can produce predictions for only one class, when objects of two or more classes are located in the same grid cell, they cannot simultaneously be identified. For example, if the input image scale is 448 \(\times\) 448, then no more than two objects can simultaneously be identified in a single 64 \(\times\) 64 grid cell, and their classes must be the same. In addition, with respect to YOLO, once the input image has passed through twenty-fourconvolution layers and four pooling layers, little detailed information can be observed in the resulting feature map. Therefore, YOLO has a relatively poor ability to detect dense small objects. Unlike YOLO, SSD adopts a multi-scale approach, which means that the feature maps that are used to detect different objects are at different scales. Because each feature map is produced from convolution results at the same level, the convolution receptive fields of the different levels must be different in size. In particular, the receptive field of a high-level convolution layer is significantly larger than those of lower layers, and the extracted information corresponding to a high-level feature layer is more abstract. The more abstract the feature extraction information is, the less detailed the information will be; thus, SSD detection is also insensitive to small objects. The formula for calculating the convolution receptive field is as follows:  
\[S_{\text{RF}}(i)=(S_{\text{RF}}(i-1)-1)N_{s}+S_{f} \tag{1}\]  
where \(S_{\text{RF}}(i)\) is the size of the convolution receptive field of the \(i\)-th layer, \(N_{s}\) is the step length, and \(S_{f}\) is the size of the filter. SSD adopts the anchor mechanism, for which the detection results are directly related to the size of the default bounding box produced from the anchors. The ratio of the default bounding box to the input image can be calculated according to the formula given in [23]. Furthermore, the relationship between the default bounding box and the input image can then be used to determine the region mapped to the input image. To measure the detection capability of a feature map, the minimal default bounding box scale is selected to observe the minimal region mapped to the input image in the feature map.