Casting-DETR: an End-to-End Network for Casting Surface Defect Detection

The task of utilizing machine vision for the detection of casting surface defects is characterized by small targets, real-time performance, and ease of mobility. The direct application of current mainstream object detection networks for defect detection presents issues of low accuracy and efficiency. Consequently, in this paper, we introduce Casting-DETR, an end-to-end network designed for casting surface defect detection. To assess and validate the model’s performance, 554 images of casting samples with surface defects were employed. Casting-DETR achieved an impressive detection rate of 98.97% on the test set, with a single image detection time of 91.5ms. Furthermore, a real-time detection system, built using PyQT6, was tested in four different environments. Casting-DETR exhibited exceptional performance, maintaining a single-frame detection time of approximately 90 ms, demonstrating the model’s high robustness and suitability for real-time detection. The Casting-DETR network proposed in this paper is an end-to-end solution with rapid convergence, superior detection accuracy, and swift detection speeds, offering a fresh perspective for similar detection tasks within the industry.