Multiscale Local and Global Feature Fusion for the Detection of Steel Surface Defects

Abstract

Steel surface defects have a significant impact on the quality and performance of many industrial products and cause huge economic losses. Therefore, it is meaningful to detect steel surface defects in real time. To improve the detection performance of steel surface defects with variable scales and complex backgrounds, in this paper, a novel method for detecting steel surface defects through a multiscale local and global feature fusion mechanism is proposed. The proposed method uses a convolution operation with a downsampling mechanism in the convolutional neural network model to obtain rough multiscale feature maps. Then, a context-extraction block (CEB) is proposed to adopt self-attention learning on the feature maps extracted by the convolution operation at each scale to obtain multiscale global context information to make up for the shortcomings of convolutional neural networks (CNNs), thus forming a novel multiscale self-attention mechanism. Afterwards, using the feature pyramid structure, multiscale feature maps are fused to improve multiscale object detection. Finally, the channel and spatial attention module and the WIOU (Wise Intersection over Union) loss function are introduced. The model achieved 78.2% and 71.9% mAP respectively on the NEU-DET and GC10-DET dataset. Compared to algorithms such as Faster RCNN and EDDN, this method is effective in improving the detection performance of steel surface defects.

Defect Identification of 316L Stainless Steel in Selective Laser Melting Process Based on Deep Learning

“…Then, for each candidate region, a CNN is employed for feature extraction and classification to determine the presence of an object and accurately localize it. Common two-stage algorithms encompass R-CNN, Fast R-CNN, and Faster R-CNN [5]. While two-stage algorithms offer enhanced accuracy, they require additional candidate region generation steps, resulting in slower computation.…”