An Improved Feature Enhancement CenterNet Model for Small Object Defect Detection on Metal Surfaces

Abstract In defect detection on metal surfaces, there are many small defects with subtle features that are difficult to distinguish from the background environment using mainstream object detection methods. To alleviate this issue, this study proposes an improved CenterNet model for enhancing the features of small defects on metal surfaces, namely MSDD. In this work, we utilize an attention mechanism to reconstruct the basic feature extraction module in the metal defect feature extraction network, aiming to enhance the focus on features related to small defects. Additionally, we redesign an efficient deconvolution module to extract multi‐scale defect feature information, capturing details of small defects at different scales. Finally, leveraging the idea of reparameterization to enhance feature representation capabilities, we optimize the output of the detection head, thereby strengthening the model's ability to capture features of small objects on the metal surface. On the NEU‐DET dataset, compared to the CenterNet baseline, the improved feature enhancement network shows a 4.9% improvement. Furthermore, with an accuracy of 80.2% compared to mainstream object detection methods, it outperforms other state‐of‐the‐art (SOTA) level object detection methods, significantly enhancing the detection accuracy and performance of the network model.