# A System for a Real-Time Electronic Component Detection and Classification on a Conveyor Belt
## 3 Experimental Results  
In Section 2, the implementation of the integrated system for the SMD component detector was described on a conveyor capable of running several state-of-the-art deep learning algorithms. In addition, we describe the collected data set and the training of the models. Using the proposed hardware setup, we conducted experimental research to find the limitations of the IT and mAP scores with various implementations of deep learning algorithms, when counting SMD components on a conveyor. The experimental results of the deep learning algorithms implemented in the proposed hardware, when detecting capacitors, are given in Table 2. As the results show, the highest AP score was displayed by the YOLOv4-tiny algorithm--97.3%--using both hardware setups. In general, the average precision between two hardware setups was less than 0.5% for the SSD-Mobilenet-V1, YOLOv3-Keras, YOLOv3-tiny, YOLOv4 and YOLOv4-tiny architectures. Tesla P100 compared to Jetson Nano showed higher AP by 1.1%, 3.3% for the YOLOv4-Scaled and YOLOv5s architectures, respectively. The inference times were different by magnitudes from 2 to 50, depending on the investigated algorithm. Lowest IT, of 10.4 ms was found for the YOLOv4 tiny algorithm when using Tesla P100 setup, and 23.8 ms for SSD-Mobilenet-V1 algorithms (AP was 66.6%) when using the Jetson Nano setup. The experimental results of the deep learning algorithms on the proposed hardware, when detecting resistors, are given in Table 3. As the results show, the highest AP was found using the YOLOv4 algorithm, 98.6% with the Jetson Nano hardware setup, and 98.5% using YOLOv4-tiny on Tesla P100. In general, the average precision between two hardware setups was less than 1% for SSD-Mobilenet-V1, YOLOv3-Keras, YOLOv4, YOLOv4-tiny, and YOLOv4-Scaled architectures, while Tesla P100 showed higher AP by 11.1% when using YOLOv3-tiny architecture and higher by 1.9% when using YOLOv5s architecture, compared to Jetson Nano. The inference times were different by magnitudes from 2 to 20, depending on the algorithm investigated. The lowest IT--10.9 ms--was found when investigating the YOLOv4 tiny algorithm using the Tesla P100 setup, and 24.4 ms for SSD-Mobilenet-V1 algorithms (AP was 75.1%) when using Jetson Nano setup. Figure 8: Frame of electronics components detection input using YOLOv4 architecture. Experimental results of deep learning algorithms implemented on the proposed hardware, when detecting diodes, are given in Table 4. As the results show, the highest AP score was displayed by the YOLOv3-Keras algorithm--88.6% and 88.7% using Tesla P100 and Jetson Nano hardware setups, respectively. In general, the AP between two hardware setups was less than 1% for SSD-Mobilenet-V1, YOLOv3-Keras, YOLOv3-tiny, YOLOv4-tiny, and YOLOv4-Scaled algorithm implementations. Tesla P100 hardware setup, compared to Jetson Nano, showed a lower AP by 19.7% when the YOLOv4 algorithm was implemented and a higher AP by 1.2% when the YOLOv5 algorithm was implemented. The inference times were different by magnitudes from 2 to 50, depending on the investigated algorithm. The lowest IT--8.2 ms--was found for YOLOv4-tiny algorithm implementation when using Tesla P100 setup, and 24.4 ms for SSD-Mobilenet-V1 algorithms (AP was 67.2%) when using the Jetson Nano setup. The experimental results of the deep learning algorithms implemented on the proposed hardware, when detecting transistors, are given in Table 5. As the results show, the highest AP score was displayed by the YOLOv4 algorithm--95.4% and 97.2% for both hardware setups. In general, the AP between two hardware setups was less than 1% for SSD-Mobilenet-V1, YOLOv3-Keras, YOLOv3-tiny, YOLOv4-tiny, YOLOv4-Scaled and YOLOv5s algorithm implementations. Tesla P100 hardware setup, compared to Jetson