The morphological characteristics of the weld joint are a core indicator for assessing the quality of aluminum alloy welds. To address issues in the morphological detection of aluminum alloy weld joints, specifically specular reflection and the uneven brightness of light stripes caused by surface contamination after welding, which lead to distortions in extracting the centerline, an anti-interference detection method using binocular vision combined with line-structured light and feature-assisted stickers is proposed. This approach integrates a U-Net neural network for welding zone localization with an improved skeletonization-gray-gravity method for sub-pixel extraction of the light stripe centerline, enabling high-quality stripe image acquisition under complex interference. The results demonstrate that the proposed method effectively suppresses interference from plate reflection and contamination, with a consistent positioning accuracy exceeding 99% for the welding zone. This approach achieves highprecision 3D reconstruction of the joint morphology, with relative errors in the detection of weld reinforcement and width not exceeding 6.0% and 4.0%, respectively, significantly enhancing the analytical accuracy and robustness of the stripe information. To investigate the relationship between process parameters and joint morphology quality in laser-metal inert gas (MIG) hybrid welding of aluminum alloy, experiments were conducted on 6-mm thick 6061 aluminum alloy plates using eight sets of parameters with different energy ratios. The joint quality was evaluated according to the ISO 10042:2018 international standard. The results indicate that as the energy ratio increases, the weld reinforcement and width gradually decrease and stabilize. When the energy ratio between the laser and the MIG arc ranges from 1.01 to 1.36, the joint quality meets the ISO 10042:2018 Level B requirements, indicating excellent joint quality. This confirms the reliability of the proposed method for detecting joint morphological features in complex environments, providing technical support for the automated assessment of aluminum alloy joint quality.