# A System for a Real-Time Electronic Component Detection and Classification on a Conveyor Belt
## 3 Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Tesla P100-16 GB**} & \multicolumn{3}{c}{**Jetson Nano 4 GB**} \\ \hline
**Model** & **AP, \%** & **FPS** & **IT, ms** & **AP, \%** & **FPS** & **IT, ms** \\ \hline SSD-Mobilenet-V1 & 67.3 & 92 & 10.9 & 67.2 & 41 & 24.4 \\ YOLOv3-Keras & 88.6 & 16 & 62.5 & 88.7 & 1 & 1000 \\ YOLOv3-tiny & 65.3 & 21 & 47.6 & 65.2 & 6 & 166.7 \\ YOLOv4 & 66.2 & 49 & 20.4 & 85.9 & 1 & 1000 \\ YOLOv4-tiny & 76.2 & 122 & 8.2 & 76.5 & 18 & 55.6 \\ YOLOv4-Scaled & 70.7 & 46 & 21.7 & 70 & 2 & 500 \\ YOLOv5s & 82.8 & 79 & 12.7 & 81.2 & 8 & 125 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental results of investigated deep learning algorithm implementations for diode detection. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Tesla P100-16 GB**} & \multicolumn{3}{c}{**Jetson Nano 4 GB**} \\ \hline
**Model** & **AP, \%** & **FPS** & **IT, ms** & **AP, \%** & **FPS** & **IT, ms** \\ \hline SSD-Mobilenet-V1 & 66.7 & 89 & 11.2 & 66.6 & 42 & 23.8 \\ YOLOv3-Keras & 90.2 & 14 & 71.4 & 90.1 & 1 & 1000 \\ YOLOv3-tiny & 83.1 & 19 & 52.6 & 82.7 & 4 & 250 \\ YOLOv4 & 94.4 & 49 & 20.4 & 94.6 & 1 & 1000 \\ YOLOv4-tiny & 97.3 & 96 & 10.4 & 97.3 & 18 & 55.6 \\ YOLOv4-Scaled & 87.8 & 44 & 22.7 & 86.7 & 4 & 250 \\ YOLOv5s & 88.6 & 89 & 11.2 & 85.3 & 8 & 125 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results of investigated deep learning algorithm implementations for capacitor detection. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Tesla P100-16 GB**} & \multicolumn{3}{c}{**Jetson Nano 4 GB**} \\ \hline
**Model** & **AP, \%** & **FPS** & **IT, ms** & **AP, \%** & **FPS** & **IT, ms** \\ \hline SSD-Mobilenet-V1 & 75 & 89 & 11.2 & 75.1 & 41 & 24.4 \\ YOLOv3-Keras & 92.7 & 15 & 66.7 & 92.7 & 1 & 1000 \\ YOLOv3-tiny & 76.3 & 18 & 55.6 & 65.2 & 6 & 166.7 \\ YOLOv4 & 97.7 & 48 & 20.8 & 98.6 & 1 & 1000 \\ YOLOv4-tiny & 98.5 & 92 & 10.9 & 98.5 & 18 & 55.6 \\ YOLOv4-Scaled & 89.1 & 44 & 22.7 & 89.1 & 2 & 500 \\ YOLOv5s & 92.9 & 70 & 14.3 & 91 & 8 & 125 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental results of investigated deep learning algorithm implementations for resistor detection. Nano, showed a lower AP of 1.8% when YOLOv4 and YOLOv5s algorithms were implemented. The inference times were different by magnitudes from 2 to 50, depending on the algorithm investigated. The lowest IT--11 ms--was found when using the YOLOv5s algorithm (AP was 68.8%) on the Tesla P100 setup, and 27.8 ms when using SSD-Mobilenet-V1 algorithm (AP was 60.5%) on the Jetson Nano setup. The experimental results of deep learning algorithms implemented on the proposed hardware, when detecting all four components, are given in Table 6. As the results show, the highest mAP score of 94.08% was demonstrated using the implementation of the YOLOv4 algorithm on Jetson Nano with an inference time of 1000 ms. Furthermore, the YOLOv4 model showed 88.433% mAP on the Tesla P100 with IT of 20.8 ms. The second-highest mAP score was found using the Jetson Nano hardware setup with implementation of the YOLOv4 algorithm; it was 88.03%, and IT was 56.4 ms, and a 87.98% mAP score with 11.2 ms using Tesla P100 platform was observed. The differences in mAP between the hardware setups of the implementations of the SSD-Mobilenet-V1, YOLOv3-Keras, YOLOv4-tiny, YOLOv4-Scaled, and YOLOv5s algorithms were less than 1.25%. Higher mAP differences were found using the YOLOv3-tiny and YOLOv4 algorithms; these were higher by 2.9% and 9.15% when comparing the Tesla P100 hardware setup with the Jetson Nano. Inference time of SSD-Mobilenet-V1 was one of the lowest--11.1 and 25.1 ms for Tesla P100 and Jetson Nano hardware setups, respectively. However, the mAP scores of the SSD-Mobilenet-V1 algorithm were 67.4% and 67.35% for Tesla P100 and Jetson Nano setups, respectively.