Enhanced Circuit Board Analysis: Infrared Image Segmentation Utilizing Markov Random Field (MRF) and Level Set Techniques

Circuit board analysis plays a critical role in ensuring the reliability of electronic devices by identifying temperature distribution, assessing component health, and detecting potential defects. This study presents a novel approach to infrared image segmentation for circuit boards, integrating Markov Random Field (MRF) and Level Set (LS) techniques to enhance segmentation accuracy and reliability. The proposed method leverages the probabilistic modeling capabilities of MRF and the contour evolution strengths of LS to achieve robust segmentation of infrared images, revealing critical thermal and structural features. Experimental results demonstrate that the proposed MRF-LS method achieves an accuracy of 86%, a precision of 92%, and a recall of 94% on a benchmark dataset of PCB infrared images. These results indicate significant improvements over conventional segmentation methods, including k-means clustering and active contour models, which yielded accuracies of 79% and 81%, respectively. Furthermore, the method shows adaptability for identifying fine-grained temperature anomalies and structural defects, with enhanced resolution for small components. The study also discusses the potential adaptability of the proposed method to other imaging modalities, highlighting its scalability and versatility. These findings underline the utility of the MRF-LS framework as a valuable tool in advancing circuit board analysis, with promising applications in quality control and predictive maintenance for the electronics industry.