Detection of Misoriented Polarized Electronic Components on PCBs Using HOG Features and Neural Networks - Jamzuri, Eko Rudiawan; Ikhsan, Habyb Nur (Journal of Applied Informatics and Computing, 2025)
Mounting misorientation on polar electronic components in printed circuit boards (PCBs) can cause malfunctions in electronic devices. This study proposes an automatic detection system that utilizes the Histogram of Oriented Gradients (HOG) feature and employs classification using an artificial neural network. The research was conducted by collecting data from PCB images featuring polar components, such as diodes, electrolytic capacitors, and transistors. Once the components are identified, the HOG features are extracted to generate feature vectors used in artificial neural network training. The experiment results show that this system can detect component orientation errors with a high degree of accuracy, achieving accuracy values of 99.5% for transistor components, 97% for electrolyte capacitors, and 93.6% for diodes. Additionally, F1 values and high precision are achieved for all three types of components. The ReLU activation function has been shown to perform best among other activation functions. While the results are promising, further research is necessary to automate the identification of component locations without relying on manual cropping processes.