## 4 Experiments and Results
### Experimental Results and Comparisons  
In order to comprehensively evaluate the superiority and effectiveness of the proposed CISPNet model, we compared it with the RCNN based algorithms, YOLO, and SSD methods. For a fair comparison, the same training data set and testing data set are used for the proposed CISPNet method and other comparison methods. Table 2 and Figure 7 show the quantitative comparison results of ten different methods, measured by AP values and PRC, respectively. R-CNN [20] is the first algorithm to use CNN for target detection, and its obvious disadvantage is that it generates about 2000 regional proposals generated by selective search to hypothesize object locations, which requires the computer to have a lot of memory space. In addition, normalized operation of regional proposals makes the algorithm lose a lot of contextual information and semantic information, resulting in positioning accuracy of 61.48% mAP and detection efficiency of 0.07 frames per second (FPS). In order to solve the problem of losing context information and semantic information in the process of image normalization by R-CNN, Fast R-CNN [21] inputs an entire image into the network and extracts a fixed-length feature vector from the feature map through the region of interest (RoI) pooling layer, which reduces the running time of the detection networks and exposes the bottleneck of regional proposal computation. Therefore, Faster R-CNN [22] introduces a region proposal network (RPN) that shares entire image convolutional features with the detection network, thus enabling nearly cost-free region proposals and achieves 68.75% mAP at the speed of 10.2 FPS. YOLO [24] and YOLO v2 [25] convert the detection problem into a single regression problem, straight from image pixels to bounding box coordinates and class probabilities. YOLO can identify targets in images more quickly than the RCNN based algorithms due to the simple network structure, however, it has great restrictions when precisely localizing certain targets, especially small ones. The detection efficiency achieves 64.2 FPS, while the detection accuracy of mAP is only 61.61%. In SSD [27], multiple feature layers are used to improve the detection performance. However, the low-level layers have the shortcoming of insufficient semantic information, and the high-level layer has the problem of large loss of feature information due to the down sampling, which may result in inaccurate detection of targets. The mAP on the testing data set achieves 74.01%. Recently, various methods have attempted to improve the accuracy of SSD, especially for small targets. DSSD [28] uses ResNet-101 [19] instead of VGG16 [18] to achieve higher accuracy. RSSD [34], DOSD [40], and ESSD [41] have improved the detection accuracy by fusing the low-level and high-level features. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **Backbone** & **Input** & **Aircraft** & **Oiltanker** & **Ship** & **mAP** & **FPS** \\ \hline R-CNN [20] & AlexNet & 1000 \(\times\) 600 & 61.67 & 62.59 & 60.17 & 61.48 & 0.07 \\ Fast R-CNN [21] & VGG16 & 1000 \(\times\) 600 & 64.21 & 65.18 & 62.22 & 63.87 & 2.1 \\ FasterR-CNN [22] & VGG16 & 1000 \(\times\) 600 & 69.77 & 69.59 & 66.88 & 68.75 & 10.2 \\ YOLO [24] & GoogleNet & 448 \(\times\) 448 & 60.26 & 58.47 & 59.14 & 59.29 & 68.4 \\ YOLO v2 [25] & Darknet & 544 \(\times\) 544 & 62.79 & 61.25 & 60.78 & 61.61 & 64.2 \\ SSD [27] & VGG16 & 300 \(\times\) 300 & 73.91 & 74.73 & 73.39 & 74.01 & 71.0 \\ DSSD [28] & ResNet101 & 321 \(\times\) 321 & 75.75 & 73.11 & 74.32 & 74.39 & 20.6 \\ RSSD [34] & VGG16 & 300 \(\times\) 300 & 77.26 & 77.18 & 77.06 & 77.17 & 40.6 \\ DSOD [40] & DenseNet & 300 \(\times\) 300 & 74.56 & 74.89 & 73.58 & 74.34 & 34.2 \\ ESSD [41] & VGG16 & 300 \(\times\) 300 & 78.76 & 79.64 & 78.09 & 78.83 & 50.1 \\ CISPNet (ours) & VGG16 & 300 \(\times\) 300 & 80.28 & 81.56 & 79.17 & 80.34 & 50.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparisons of speed and accuracy based on remote sensing image dataset tests.