<p>This work presents a novel approach to improving the robotic quality inspection of the spray coating process in the aerospace industry by integrating computer vision with robotic systems. While spray coating is essential for providing protective and aesthetic coatings, due to the challenges and complexity of the aerospace industry, it frequently encounters issues such as incomplete coverage, paint defects, and surface imperfections, which can compromise quality and increase the need for rework. To address this, a methodology that utilises a cutting-edge computer vision technique based on YOLOv10 for real-time defect localisation is proposed, targeting issues such as uneven thickness and missed areas. Once the camera is calibrated, the results of defect localisation achieve a multi-class mean Average Precision of 99%. Furthermore, this work presents a framework that demonstrates how positional information and classification results can be utilised to automatically generate path planning and control actions for an intelligent spray coating system. This innovation advances the state of knowledge in the field, which has previously relied only on image classification.</p>