A Method for Detecting Small Target Weld Defects Based on Feature Reorganization Network

Abstract In the process of intelligent weld seam inspection, small weld seams are often prone to false detections or omissions. Existing methods simply concatenate feature segments during the upsampling process without analyzing the importance of each feature segment, leaving room for further improvement in detection accuracy. To address these issues, this study proposes a Feature Reorganization Network (FRNet) for detecting small target weld defects. First, the C2f-Faster-EMA feature extraction module is designed using GSConv convolution, and the LSKNet is introduced to dynamically adjust the receptive field of the backbone in the Backbone section, enhancing the model's ability to extract small target features. Then, a lightweight CARAFE upsampling module is designed in the neck network, which retains more detailed information through feature reorganization and feature expansion, and introduces the parameter-free attention mechanism SimAM to fully capture the contextual information of small targets, thereby enhancing the proposed model's ability to extract small target features. Finally, the GIoU boundary loss function is used to improve the network's bounding box regression performance, achieving intelligent detection of small target weld defects. Experimental results show that the proposed method achieves a mean average precision, parameter count, and computation volume of 85.6%, 2.5M, and 7.0G, respectively, for weld defect detection, outperforming the comparison models and meeting the requirements of practical engineering applications.