# A System for a Real-Time Electronic Component Detection and Classification on a Conveyor Belt
## 4 Discussion  
The highest mAP score under 70 ms IT for all components was achieved using the YOLOv4-tiny algorithm on a Jetson Nano platform: 88.03% mAP. Naturally, a larger YOLOv4 network showed a higher mAP score of 94.08% mAP, but at IT of 1000 ms. Our experimental results are in line with similar research [75]. The authors of this paper implemented and investigated the performance of several deep learning algorithms (including YOLOv4 and YOLOv4-tiny) for an electronic component conveyor belt. Their findings show \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 & 67.4 & 89.75 & 11.1 & 67.35 & 40 & 25.1 \\ YOLOv3-Keras & 84.6 & 15 & 66.8 & 84.6 & 1 & 1000 \\ YOLOv3-tiny & 72.05 & 19.25 & 52.1 & 69.15 & 5.5 & 187.5 \\ YOLOv4 & 88.43 & 48 & 20.8 & 94.08 & 1 & 1000 \\ YOLOv4-tiny & 87.98 & 93.75 & 11.2 & 88.03 & 17.75 & 56.4 \\ YOLOv4-Scaled & 79.3 & 43.75 & 22.9 & 78.83 & 2.5 & 437.5 \\ YOLOv5s & 83.28 & 82.25 & 12.3 & 82.03 & 8 & 125 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Experimental results of investigated deep learning algorithm implementations for detection of capacitor, resistor, diode and transistor SMD components. \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 & 60.6 & 89 & 11.2 & 60.5 & 36 & 27.8 \\ YOLOv3-Keras & 66.9 & 15 & 66.7 & 66.9 & 1 & 1000 \\ YOLOv3-tiny & 63.5 & 19 & 52.6 & 63.5 & 6 & 166.7 \\ YOLOv4 & 95.4 & 46 & 21.7 & 97.2 & 1 & 1000 \\ YOLOv4-tiny & 79.9 & 65 & 15.4 & 79.8 & 17 & 58.8 \\ YOLOv4-Scaled & 69.6 & 41 & 24.4 & 69.5 & 2 & 500 \\ YOLOv5s & 68.8 & 91 & 11 & 70.6 & 8 & 125 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experimental results of investigated deep learning algorithm implementations for transistor detection. that YOLOv4-tiny achieves slightly higher mAP. However, the hardware and mounting type of electronic components were different from our work. In their research (compared to ours), the authors used a more powerful hardware system, with an Intel Core i7-8700 CPU, NVIDIA TITAN Xp GPU, 32 GB RAM. In addition, the authors intend to deploy their proposed method on an embedded computing device in their future work. Furthermore, the detected electronic components were of through-hole technology type, which is visually much easier to distinguish; in particular, differences between resistors and capacitors are clearly identifiable. In another work [60], the results of the experimental research showed that YOLOv4 architectures achieve a higher mAP score compared to YOLOv3 when detecting SMD components on a PCB board. However, the hardware used in this investigation is not reported. The detection of electronic components on the conveyor did not reach 100%, due to the three main challenges of the problem. The first is the difficulty of distinguishing the SMD components from one another; the main differences between the components are the size and shape of the leads. The second challenge is to meet the requirements of real-time detection: while we run a real-time detection, the conveyor moves and changes its velocity. Thus, it adds noise to the input image (e.g., blur, unwanted reflections from lightning). Such noises introduce distortions and make it difficult to distinguish components that already have small visual differences. The third challenge is the requirement that the model is able to operate in the embedded computing device. Such devices usually have limited computing power and are required to operate in real-time. In practice, it is a common setup to reduce deployment and scaling costs. Thus, these three challenges decrease the performance of the detector.