Quality Detection Model for Automotive Dashboard Based on an Enhanced Visual Model

For an enterprise, product quality is the foundation of its further development. Therefore, how to detect the quality of the products produced by the assembly line and accurately identify the problematic parts has become an increasingly concerned issue for enterprises. In this paper, we propose a novel quality detection model combining the latest YOLOv5 model and convolutional neural network, which can further improve the recognition precision and accuracy of YOLOv5 on the basis of its lightweight and high recognition efficiency. The proposed model can meet the needs of complex quality problems that are difficult to detect directly in assembly-line products. In the experiment, our model can detect the automotive dashboard and judge whether the cable buckle is connected in place. The accuracy of each buckle in the picture being correctly detected is more than 98%, the classification accuracy is also expected to reach 98%.