# CP-SSD: Context Information Scene Perception Object Detection Based on SSD
## 4 Analysis and Discussion of Experimental Results
### Comparison of Methods  
In Table 3, we compared the R-CNN, YOLO, and SSD methods on the VOC 2007 test dataset. For RCNN based algorithms, RCNN [3] is the first algorithm to use CNN for object detection. It has great shortcomings in the selection of region proposals. Too many region proposals are selected by the algorithm, which requires a lot of memory and the normalization process of the input network makes the algorithm lose a lot of context information and features, resulting in only 50.2% positioning accuracy. In order to cope with solving the feature loss problem of R-CNN in image normalization, Fast-RCNN [18] inputs the whole image into the network and extracts fixed-length feature vectors from the feature map through the region of interest (RoI) pool layer. The resulting classification and coordinate information eventually increased the accuracy to 70.0%. However, Faster-RCNN still does not solve the problem caused by 2000 regional proposals generated by selective search. Therefore, in Faster-RCNN [19] algorithms, the RPN module is proposed, which utilizes 9 kinds of anchors with different area ratios and applying CNN to complete the object detection compeletely. The mAP reached 76.4%. In YOLO [21] algorithms an end-to-end network is used to remove the selection of regional proposal individually, combining the selection of regional proposal with the object detection network. Due to the simple network structure, the speed of object detection is much higher than that of the RCNN based algorithms but there are great restrictions to the position and size of the object. The detection effect of mAP is only 57.9%, especially for small objects. In SSD [4], low-level feature maps are separated to improve the detection effect of small objects but there are shortcomings of insufficient semantic information. The additional detection layer features use the downsampling method to increase the receptive field but the resolution of the downsampling reduction feature map \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Method** & **mAP** & **Aero** & **Bike** & **Bird** & **Boat** & **Bottle** & **Bus** & **Car** & **Cat** & **Chair** & **Cow** \\ \hline SSD [4] & 77.2 & 78.8 & 85.3 & 75.7 & 71.5 & 49.1 & 85.7 & 86.4 & 87.8 & 60.6 & 82.7 \\ SSD+CISP & 77.6 & 80.5 & 85.1 & 76.0 & 71.1 & 52.9 & 86.1 & 86.4 & 87.1 & 61.3 & 81.8 \\ \hline
**Method** & **mAP** & **Table** & **Dog** & **Horse** & **Mbike** & **Person** & **Plant** & **Sheep** & **Sofa** & **Train** & **tv** \\ \hline SSD [4] & 77.2 & 76.5 & 84.9 & 86.7 & 84.0 & 79.2 & 51.3 & 77.5 & 78.7 & 86.7 & 76.2 \\ SSD+CISP & 77.6 & 77.7 & 84.5 & 86.4 & 85.0 & 79.0 & 53.0 & 76.5 & 80.9 & 85.5 & 77.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Test results of SSD and SSD+CISP. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Method** & **mAP** & **Aero** & **Bike** & **Bird** & **Boat** & **Bottle** & **Bus** & **Car** & **Cat** & **Chair** & **Cow** \\ \hline SSD [4] & 77.2 & 78.8 & 85.3 & 75.7 & 71.5 & 49.1 & 85.7 & 86.4 & 87.8 & 60.6 & 82.7 \\ SSD+SAB & 77.6 & 81.3 & 84.9 & 75.7 & 72.0 & 50.7 & 85.4 & 86.4 & 87.9 & 61.8 & 82.3 \\ \hline
**Method** & **mAP** & **Table** & **Dog** & **Horse** & **Mbike** & **Person** & **Plant** & **Sheep** & **Sofa** & **Train** & **tv** \\ \hline SSD [4] & 77.2 & 76.5 & 84.9 & 86.7 & 84.0 & 79.2 & 51.3 & 77.5 & 78.7 & 86.7 & 76.2 \\ SSD+SAB & 77.6 & 77.7 & 85.6 & 87.7 & 81.9 & 79.1 & 52.4 & 77.5 & 81.6 & 84.7 & 76.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Test results of SSD and SSD+semantic activation block (SAB). causes a large loss of feature information and the mAP on the testing dataset is only 77.2%. The mAP of the CP-SSD on the testing dataset reached 77.8%, which is 0.6% higher than the original SSD. In CP-SSD, we use the CISP module to fuse context information and prior information between different scales and different sub-regions from feature maps \(U\).