# A System for a Real-Time Electronic Component Detection and Classification on a Conveyor Belt
## 4 Discussion
In our work, we investigated the detection of four different electronic components, these being capacitors, resistors, diodes, and transistors. In practice, resistors and capacitors contribute to the largest volume of electronic components on a conveyor. The average precision for the detection of capacitors and resistors was 97.3% and 97.7%, respectively. Several recently published research papers accentuate the challenge of detecting these two components with a higher that 96% mAP [47; 58; 76]. Our results show a considerable improvement in the detection of resistors and capacitors. To deploy a deep learning algorithm in a real-time object detection system, one needs to choose an algorithm that can process a certain number of images per second. In practice, FPSs above 15 are sufficient when detecting and counting SMD components on a conveyor (or inference time below 70 ms). Similar research works, proposing the automatic identification of electronic components, deploy their system on powerful graphical processors including NVIDIA TITAN Xp GPU [75], NVIDIA Quadro P5000 [58], NVIDIA GTX 1050 [53]. On the contrary, our proposed system setup includes a microcomputer running on an embedded device. Based on our results, the Tesla P100 platform achieved the desired inference time for all models investigated. However, considering scale, price and performance ratio, a more realistic approach would be to deploy such a system on a Jetson Nano microcomputer. In this case, only several algorithms achieved mAP above 90%, with ITs below 70 ms. Our results show that YOLOv4-tiny is the best choice to implement in an integrated system with a Jetson Nano microcomputer when detecting four types of SMD components; when detecting capacitors, the algorithm achieves 97.3% AP at 55.6 ms IT; when detecting resistors, the algorithm achieves 98.5% AP at 55.6 ms IT. Based on the experimental results, we can see that Tesla P100 detects objects at higher FPS. One way to speed up the FPS for the Jetson Nano would be pruning technique. In our work, we avoided using pruning of the models. Instead, we investigated the performance of deploy-ready models, such as YOLOv4-tiny, SSD-Mobilenet-V1, YOLOv3-tiny, YOLOv5s. Therefore, a trade-off between IT and mAP can be noticed when comparing pruned models with their counterparts (e.g., YOLOv4 and YOLOv4-tiny), which is the expected outcome, since reducing the size of model improves the IT and reduces the mAP. However, when comparing similarly sized, but different architectures (e.g., SSD-MobileNet-V1 and YOLOv3-Keras) a trade-off between IT and mAP was not found. In theory, deployed models should achieve similar AP scores regardless of the hardware they are deployed with. However, our results show that several algorithms, such as YOLOv3-tiny, YOLOv4 achieve different AP scores based on the hardware they are deployed with. Such dependencies may arise from custom hardware-based neural network accelerators [77]. However, future research directions should not only aim to improve the AP and IT scores in case of real-time SMD component detection, but also investigate the dependencies of the AP score based on the deployed platform.