ACPP-Net: Enhancing Strip Steel Surface Defect Detection With Efficient Adaptive Convolution and Channel-Spatial Pyramid Pooling

Abstract

As an indispensable material in modern industry, steel requires real-time surface defect detection to ensure high-quality manufacturing. However, steel surface defects present significant challenges due to their tiny size, diverse morphology, and uneven feature distribution. To address these challenges and satisfy the balance between accuracy and detection speed, an efficient steel strip surface defect detection network, ACPP-Net, is proposed in this study. Firstly, Adaptive Ghost Convolution, the LM-block, is introduced to meet the need for rapid steel defect detection. By integrating Adaptive Ghost Convolution, this module increases efficiency by reducing redundant information acquisition and adaptively assigning weights to defect features. Secondly, a novel feature enhancement module, FEM-block, is proposed to address the complexity of steel defects and distinguish their subtle differences. This module excels at capturing complex defect textures, aiding in the accurate differentiation of various defects. Additionally, a channel spatial pyramid pooling (CSPP) module is incorporated into the final part of the backbone network. This module effectively helps the network understand the characteristics and distribution of steel defects. Extensive experiments on the NEU-DET and GC10-DET datasets demonstrate ACPP-Net’s superior performance, achieving 82.1% and 71.1% mAP respectively, while maintaining real-time detection capabilities. The detection performance of this model is superior to other methods. These results demonstrate the model’s high accuracy and real-time detection capabilities, thus contributing to efficient and high-quality steel detection processes.

Keywords

• Surface defect detection
• ghost convolution
• deep learning
• computer vision
• attention mechanism