# An Intelligent Real-Time Object Detection System on Drones
## 4 Results and Discussion
### Intelligent Object Detection  
We perform object detection and display some detection results in this section. We aim to find the targets of interest in an area using the drone and report detected objects in  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**NN model** & **Weight Size** & **Weight Loading Time** & **Processing Time** \\ \hline
**ResNet-50** & 147 MB & 16.01 s & 0.8 s \\
**Yolov3** & 384 MB & 39.31 s & 1.17 s \\
**Yolov3-tiny** & 51 MB & 5.88 s & 0.13 s \\ \hline \hline \end{tabular}
\end{table}
Table 4: NN engine speed for different NN models.  
Figure 8: The detailed time decomposition of various NN models using ten images.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Implementation** & **Virtex-Yolo** & **Zynq-Yolo** & **Req-Yolo** & **Our Work** \\  & [9] & [10] & [11] & \\ \hline Device Type & Xilinx Virtex-7 485t & Zynq 7020 & ADM-7V3 FPGA & Kintex-7 410T \\ \hline Clock Freq. (MHz) & 140 MHz & 150 MHz & 200 MHz & 100 MHz \\ \hline Speed (FPS) & 21 & 8 & 208 & 8 \\ \hline Power (W) & N/A & N/A & 23 & 3.529 \\ \hline Latency (ms) & 970 & 125 & N/A & 130 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Benchmarking with state-of-the-art object detection using FPGA accelerators.  
real time. Note that due to the characteristics of the Yolo model, it is inevitable to produce wrong object detection results.  
We randomly select some objects to evaluate if the system can increase the confidence score when it is below the specified value. Note that Figures 9-12 are from the camera and are not publicly available. Figure 9 shows an ordinary person-detection result, where some bikes are misclassified as a person.  
With our intelligent object detection techniques, we empirically set the confidence score threshold to 0.8 and the drone approaches the detected target when the score is less than the threshold. On the one hand, it can generate more detection results, such that the wrong detection results may be spotted. On the other hand, it produces more pixels as the drone approaches, which helps improve the accuracy of object detection.  
Figure 10 displays the person-detection result using intelligent object detection techniques. We can see that the confidence score of the person has improved from 0.74 to 0.90. In addition, the misclassified bikes have been corrected from person to bicycle with a confidence score of 0.50.  
In addition to a person, we also demonstrate the effectiveness of the system for cars. Figure 11 illustrates the ordinary car detection and most confidence scores are between 0.50 and 0.70. Figure 12 shows the intelligent object detection mechanism for the car and the confidence scores have been varied, where the top score has been increased from 0.7 to 0.84.  
From the experimental results, we can see that the system is intelligent in two ways. On the one hand, Table 5 validates that our system can intelligently process the image detection and consumes low power. On the other hand, Figures 9-12 indicates that when the TOI contains few pixels, the resulting confidence score is low and our system intelligently approaches the TOI to increase the object detection score, which leads to a larger TOI in the image.  
Figure 9: The ordinary person-detection result.  
It is worth mentioning that since our system can run various neural network models and signal processing tasks, the system is not limited to person and car detection and it can also be used for other object detection or even other types of tasks. For example, it can be adopted to accelerate the CNN model that monitors littering activities [32], and deal with thermographic fault diagnosis tasks using a feature-extraction technique [33].  
Figure 11: The ordinary car-detection result.  
Figure 10: The intelligent person-detection result.