# CP-SSD: Context Information Scene Perception Object Detection Based on SSD
## 4 Analysis and Discussion of Experimental Results
### Detection Examples  
In Figure 5, we visualize some of the images. The localization results of CP-SSD were compared with the original SSD. As shown in Figure 5, in the upper two rows of images, SSD cannot locate people on horseback and boat, while CP-SSD can. CP-SSD uses the semantic activation module to capture more prior information before downsampling, so it can understand the image more accurately. In the lower two rows of images, the boats and buildings in the image are similar in shape and color and the color of the bird is similar to the surrounding environment. SSD cannot accurately detect the position of ships and birds because of the lack of understanding of the scene. CP-SSD can more fully understand the contextual prior information so that it can better distinguish the background and the detected objects, and determine the location of the ship and the bird through the contextual information.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Method** & **backbone** & **mAP** & **Aero** & **bike** & **bird** & **boat** & **bottle** & **bus** & **car** & **cat** & **chair** & **cow** \\ \hline RCNN [3] & AlexNet & 50.2 & 67.1 & 64.1 & 46.7 & 32.0 & 30.5 & 56.4 & 57.2 & 65.9 & 27.0 & 47.3 \\ Fast [18] & VGG16 & 70.0 & 77.0 & 78.1 & 69.3 & 59.4 & 38.3 & 81.6 & 78.6 & 86.7 & 42.8 & 78.8 \\ Faster [19] & VGG16 & 73.2 & 76.5 & 79.0 & 70.9 & 65.5 & 52.1 & 83.1 & 84.7 & 86.4 & 52.0 & 81.9 \\ Faster [8] & ResNet101 & 76.4 & 79.8 & 80.7 & 76.2 & 68.3 & 55.9 & 85.1 & 85.3 & 89.8 & 56.7 & **87.8** \\ RONN384++ [25] & VGG16 & 77.6 & **86.0** & 82.5 & 76.9 & 69.1 & **59.2** & 86.2 & 85.5 & 87.2 & 59.9 & 81.4 \\ [PERSON] et al. [26] & VGG16 & 76.4 & 79.3 & 80.5 & 76.8 & **72.0** & 58.2 & 85.1 & **86.5** & **89.3** & 60.6 & 82.2 \\ YOLO [21] & Darknet & 57.9 & 77.0 & 67.2 & 57.7 & 38.3 & 22.7 & 68.3 & 55.9 & 81.4 & 36.2 & 60.8 \\ SSD321 [4] & ResNet101 & 77.1 & 76.3 & 84.6 & 79.3 & 64.6 & 47.2 & 85.4 & 84.0 & 88.8 & 60.1 & 82.6 \\ SSD300 [4] & VGG16 & 77.2 & 78.8 & 85.3 & 75.7 & 71.5 & 49.1 & 85.7 & 86.4 & 87.8 & 60.6 & 82.7 \\ \hline CP-SSD(SSD+CISP+SAB) & VGG16 & **77.8** & 83.9 & **86.3** & **80.1** & 69.9 & 50.6 & **86.5** & 85.6 & 88.4 & **62.8** & 79.4 \\ \hline
**Method** & **backbone** & **mAP** & **table** & **dog** & **horse** & **mbike** & **person** & **plant** & **sheep** & **sofa** & **train** & **tv** \\ \hline RCNN [3] & AlexNet & 50.2 & 40.9 & 66.6 & 57.8 & 65.9 & 53.6 & 26.7 & 56.5 & 38.1 & 52.8 & 50.2 \\ Fast [18] & VGG16 & 70.0 & 68.9 & 84.7 & 82.0 & 76.6 & 69.9 & 31.8 & 70.1 & 74.8 & 80.4 & 70.4 \\ Faster [19] & VGG16 & 73.2 & 65.7 & 84.8 & 84.6 & 77.5 & 76.7 & 38.8 & 73.6 & 73.9 & 83.0 & 72.6 \\ Faster [8] & ResNet101 & 76.4 & 69.4 & **88.3** & **88.9** & 80.9 & 78.4 & 41.7 & **78.6** & 79.8 & 85.3 & 72.0 \\ RONN384++ [25] & VGG16 & 77.6 & 73.3 & 85.9 & 86.8 & 82.2 & 79.6 & 52.4 & 78.2 & 76.0 & 86.2 & **78.0** \\ [PERSON] et al. [26] & VGG16 & 76.4 & 69.2 & 87.0 & 87.2 & 81.6 & 78.2 & 44.6 & 77.9 & 76.7 & 82.4 & 71.9 \\ YOLO [21] & Darknet & 57.9 & 48.5 & 77.2 & 72.3 & 71.3 & 63.5 & 28.9 & 52.2 & 54.8 & 73.9 & 50.8 \\ SSD321 [4] & ResNet101 & 77.1 & 76.9 & 86.7 & 87.2 & **85.4** & 79.1 & 50.8 & 77.2 & **82.6** & **87.3** & 76.6 \\ SSD300 [4] & VGG16 & 77.2 & 76.5 & 84.9 & 86.7 & 84.0 & 79.2 & 51.3 & 77.5 & 78.7 & 86.7 & 76.2 \\ \hline CP-SSD(SSD+CISP+SAB) & VGG16 & **77.8** & **77.9** & 83.1 & 88.1 & 84.5 & **80.0** & **53.5** & 74.1 & 77.1 & 86.4 & 77.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Test results of CP-SSD in PASCAL VOC2007.