Explainable attention-based fused convolutional neural network (XAFCNN) for tire defect detection: an industrial case study

Abstract

Abstract Ensuring tire quality is crucial in the manufacturing industry, particularly for race cars, where defective tires present a significant safety risk. Visual inspection for defects in tires is crucial; however, identifying defects in complex, textured tires has been proven to be a challenging task. This paper tackles this challenge by introducing XAFCNN, an Explainable Attention-based Fused Convolutional Neural Network for tire defect detection. XAFCNN’s novel architecture, including a Special Attention Module (SAM) and custom CNN structure, coupled with Grad-CAM visualization, prevents overfitting, enhances local feature mapping, enables detection of small defects, and proffers valuable insights into the model’s reasoning, enabling confident interpretation of its predictions. The model was trained on a dataset from a leading global tire manufacturer, including 38,710 x-ray images of defective tires and 83,985 defect-free tire images, covering 15 defect types and 50 design patterns. The results demonstrate the model’s exceptional performance compared to literature, achieving a recall rate of 86.85%, a precision of 98.5%, an F1 score of 92.31%, and an overall accuracy of 95.40%. This research, with its substantial dataset and high-performing model, advances automated tire defect detection, satisfying the industry’s need for accurate and reliable inspections, ultimately enhancing human safety.