# Deep-Learning Based Algorithm for Detecting Targets in Infrared Images
## 5 Experiment and Analysis of Experimental Results
### Experimental Analysis
Therefore, the proposed infrared image target detection model uses a 1+3+5 convolution layer combination. 2. _Experiment II:_ To verify the performance of the two multiscale feature extraction mechanisms and CMF Net, we carried out three experiments, and the experimental results are shown in Figure 8. We found that our target detection model CMF Net has a great improvement. The mAP of CMF Net improved about 6.8% and 4.4% compared to the first multiscale feature extraction mechanism and the second multiscale feature extraction mechanism, respectively. Although the target detection accuracy of CMF Net decreased in the bicycle category, it improved by 6.1% and 24% in the car and person categories, respectively, compared to the first multiscale feature extraction mechanism, and 13.2% and 9.3%, respectively, compared to the second multiscale feature extraction mechanism, CMF Net's accuracy only in the bicycle target. The accuracy of CMF Net is reduced by 9.6% compared to the first multiscale feature extraction mechanism and 9.3% compared to the second multiscale feature extraction mechanism. This experimental result illustrates the importance of feature fusion based on two multiscale feature extraction mechanisms compared to one multiscale feature extraction mechanism alone to improve the performance of the target detection model. 3. _Experiment III:_ To fully prove the correctness of our multiscale feature extraction strategy, we still adopted the idea of transfer learning to migrate the pre-training networks of Faster R-CNN, YOLO, and SSD, which are currently popular in the \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Layers** & **Single 1** & **Single 2** & **Single 3** & **Group 1** & **Group 2** & **Group 3** & **Group 4** \\ \hline mAP & 0.514 & 0.605 & 0.583 & 0.567 & 0.618 & **0.636** & **0.661** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of combining different convolutional layers. visible light domain, to FLIR infrared driving image dataset, continue training until the model converges. The experimental results are shown in Figure 9 We evaluate the performance of CMF Net on the test set of FLIR infrared driving image dataset. Using the above methods, we get about 71% of mAP by CMF Net, 58% by Faster R-CNN, 65% by YOLO, and 54% by SSD. Compared with other common target detection model's mAP, our multiscale feature fusion model CMF Net achieved significant improvement in accuracy, about 13 percentage points higher than Faster R-CNN's mAP, about 6 percentage points higher than YOLO's mAP, and about 17 percentage points higher than SSD's mAP. In FLIR infrared driving image dataset, due to the different shooting distances, the size of the car, person, and bicycle targets in the infrared image is different, which has significant multiscale characteristics. Our model adopts two multiscale feature extraction mechanisms and two-level feature fusion methods, which makes the final output of the backbone network contains rich visual features and semantic features, so the detection  
Figure 8: Performance comparison between CMF Net and two multiscale feature extraction mechanisms. Figure 9: Performance comparison between CMF Net and other target detection models. accuracy of car, person, and bicycle is far higher than the other three networks. The accuracy of CMF Net on car target is 11%, 5%, and 12% higher than Faster R-CNN, YOLO, and SSD, respectively; the accuracy on person target is 23% and 17% higher than Faster R-CNN and SSD, respectively, and the accuracy on bicycle target is 4%, 16%, and 21% higher than Faster R-CNN, YOLO, and SSD respectively. Compared with YOLO, the accuracy of the personal target is reduced by 3%. The importance of the combination of two multiscale feature extraction mechanisms and two-level feature fusion methods is fully proved. As shown in Figure 10, the target detection result of CMF Net is on the left, and the target detection result of Faster R-CNN is on the right.