Artificial intelligence (AI) can improve the quality control in the automotive industry. Reliable inspection methods are essential in manufacturing environments, as a single defect can damage a company’s reputation. This work presents a practical solution using computer vision and deep learning to detect small screws installed in the wheel boxes of vehicles on a moving assembly line. The system combines a custom image acquisition setup with preprocessing techniques to improve contrast and clarity, followed by object detection using the YOLOv8 algorithm. Synthetic data generated to train the model effectively allowed for more robust performance without relying solely on real-world samples. The system tested outcomes with a screw detection accuracy of 92%, compared to around 71% accuracy from traditional visual inspections. That result suggested that AI-based approaches can significantly enhance quality assurance processes in automotive manufacturing, particularly in fast-paced production settings.