# Deep-Learning Based Algorithm for Detecting Targets in Infrared Images
## 5 Experiment and Analysis of Experimental Results
### Experimental Analysis  
The training set of Infrared image dataset FLIR contains 7860 IR images, and the test set has 1360 Infrared images. To facilitate the experiment, we converted the image \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Model** & \
\begin{tabular}{l} **Backbone** \\ **Network** \\ \end{tabular} & **RPN** & **ROI Pooling** &
\begin{tabular}{l} **Classification** \\ **Network** \\ \end{tabular} & **Total** \\ \hline Faster R-CNN & 14,714,688 & 2,382,893 & 0 & 11,961,1408 & 136,708,989 \\ CMF Net & 17,077,824 & 7,691,309 & 0 & 127,279,632 & 152,048,765 \\ CMF-3 DLSTM & 17,077,824 & 7,691,309 & 0 & 15,992,336 & 40,761,469 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental environment. \begin{table}
\begin{tabular}{l l} \hline \hline Content & \begin{tabular}{l} Synced annotated thermal imagery and non-annotated RGB imagery for reference. Camera centerlines approximately 2 inches apart and collimated to minimize parallax \\ \(>\)10 K from short video segments and random image samples. \\ \end{tabular} & \begin{tabular}{l} Recorded at 30 Hz. Dataset sequences sampled at 2 frames/s or 1 frame/s. Video \\ annotations were performed at 30 frames/s recording. \\ 10,228 total frames and 9214 frames with bounding boxes. \\ 1. \\ 2. \\ Frame Annotation Label Totals \\ 3. \\ 4. \\ 5. \\ \end{tabular} & \begin{tabular}{l} Person (28,151); \\ 2. \\ 3. \\ 5. \\ \end{tabular} & \begin{tabular}{l} 2. \\ 4(46,692); \\ 4. \\ 5. \\ \end{tabular} & \begin{tabular}{l} 2. \\ 3. \\ 5. \\ \end{tabular} & \begin{tabular}{l} 2. \\ 4(4457); \\ 2. \\ 5. \\ \end{tabular} \\ Driving Conditions & \
\begin{tabular}{l} Day (60\%) and night (40\%) driving on Santa Barbara, CA area streets and highways from \\ November to May with clear to overcast weather. \\ 1. \\ 2. \\ 3. \\ 4. \\ 5. \\ \end{tabular} &
\begin{tabular}{l} Thermal—14-bit TIFF (no AGC); \\ 2. \\ 3. \\ 4. \\ 5. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Dataset specifications. annotation files of the Infrared image from JSON format (MSCOCO format) to XML format (VOC2007 format). We conducted a total of four experiments. The first set of experiments tested the performance of the Faster R-CNN target detection model under various network layer combinations. The second group of experiments analyzed the performance comparison between CMF Net (Faster R-CNN model that adopts two multiscale feature extraction mechanisms and carries out feature fusion) and those using two multiscale feature extraction mechanisms alone. The third group of experiments analyzed the performance comparison between CMF Net and other target detection models. The fourth group of experiments analyzed the performance comparison between CMF-3 DLSTM (using 3 DLSTM network to replace the full connection layer in CMF Net) and other target detection models. 1. _Experiment I:_ The performance of the target detection model depends mainly on whether the feature map contains rich features or not. To investigate which network layers and network layer combinations can make the model the best performance, we conduct seven sets of tests based on the Faster R-CNN target detection model. The final target detection performance of the feature maps output by convolutional layer 1 (single 1), convolutional layer 3 (single 2), and convolutional layer 5 (single 3) are first tested separately. Then, the target detection is performed for the feature maps output by the convolutional layer combination 1+2+3 (Group 1) and 3+4+5 (Group 2), respectively. Finally, the target detection is performed for the feature maps output by the convolutional layer combination 1+3+5 with two different multiscale feature extraction mechanisms (Group 3 and Group 4). The experimental results are shown in Table 4.  
Experimental results show that the target detection model of convolution layer combination 1+3+5 with two different multiscale feature extraction mechanisms (Group 3 and Group 4) has better detection performance on FLIR.