Solar energy is a clean and renewable source of energy produced by solar panels. Solar panels deteriorate over time, resulting in generation of faults. Faults reduce the overall power generation capacity of photovoltaic (PV) plants. A variety of atmospheric and functional conditions contribute to the formation of hotspots on solar panels, indicating an increase in temperature and resulting in lower efficiency. Early detection of faults during PV module inspection and monitoring is critical for improving the efficiency, reliability, and safety of PV systems. Thermal imaging is a non-contact, non-destructive, efficient, and effective technique. With thermal image analysis, probable problem areas can be identified and fixed before actual failures or problems occur, resulting in lower costs and less human labor. In this study, the effect of pre-processing techniques on fault detection in thermal images is studied and a comparative fault detection and demarcation method is proposed. This study investigates the impact of pre-processing techniques such as filters and histogram equalization on fault detection and demarcation accuracy. Five different types of faults, such as single cell, multicell, diode, dust/shadow, and PID hotspot are detected. For fault detection, two segmentation techniques, histogram-based color thresholding and RGB color channel-based thresholding, are applied to thermal images of solar panels. Intersection over Union (IoU) is used to determine the efficiency of fault detection and demarcation techniques. Application of filters and histogram equalization on the dataset provided increased contrast and highlighted the faulty area of the thermal image more prominently. Overall, images processed with a bilateral filter and histogram equalization performed better for fault detection and demarcation than other filters. This technique resulted in IoU values of 0.35, 0.14, 0.31, 0.54 and 0.32 for diode, dust, multicell, single cell & PID hotspots respectively