The rapid launch of major low-Earth orbit megaconstellations has led to a proliferation of satellite streaks in survey images, posing challenges for trans-Neptunian object (TNO) observation. The presence of this foreground of streaks in wide-field survey images complicates TNO detection and tracking methodologies, potentially obscuring TNOs from detection, compromising astrometric precision, skewing photometric measurements, and leading to distorted population statistics. Accurate detection of streaks is therefore key for a comprehensive understanding of their impact. We assess the effect of streaks on the CFHT Large Program CLASSY: the Classical and Large-A Solar SYstem survey through observational data analysis, exploring various strategies aimed at detecting and mitigating their influence. As part of this assessment, we evaluated five distinct existing methodologies for detecting and localizing satellite streaks in CFHT MegaCam CLASSY images. Among these methodologies, we included a standard Hough Transform based approach, ASTRiDE, and employed three different deep neural network architectures for streak detection. A standardized execution and testing framework was developed to ensure uniformity across evaluations, encompassing image manipulation and model output processing. Leveraging a labelled dataset of images containing streaks, the performance of each approach was quantitatively evaluated. Examination of the results revealed challenges stemming from significant variability in streak width, brightness levels, and time-varying morphology within individual satellite passes, which resulted in detected streaks of incorrect widths, streak angle misalignments, and high false positive rates. These challenges highlight the need for the development of more robust methodologies to address them effectively. Moving forward, we intend to refine the most promising approaches identified during evaluation and utilize survey-specific training data to improve the effectiveness of streak detection and localization.