A Lightweight Classification Method Based on You Only Look Once Version 8 and Convolutional Block Attention for Scanning Electron Microscopy Images of Metal Fracture Surfaces

In the failure analysis of metallic materials, the observation of fracture-surface morphology by scanning electron microscopy (SEM) is an important means of determining the fracture mechanisms. In practice, however, interpretation of fracture-surface images relies heavily on expert experience and is readily affected by subjective factors when large batches of images must be examined. To address this issue, this paper proposes a classification model based on You Only Look Once version 8 (YOLOv8) and the convolutional block attention module (CBAM) for SEM images of metal fracture surfaces, aiming to identify four typical fracture-surface categories: cleavage, fatigue, dimple, and intergranular fracture. Considering that SEM images are mostly grayscale texture images, the model emphasizes the preservation of brightness, local texture, and edge-contour information during input processing and training augmentation, and introduces CBAM to optimize the channel and spatial responses of feature maps. Experimental results showed that at an input resolution of 1024, the YOLOv8-CBAM model achieved a Top-1 accuracy of 97.92% with only 1.53 M parameters. The proposed model achieved a favorable balance between observed classification performance and model complexity compared with the evaluated convolutional neural network (CNN) baselines. In addition, the gradient-weighted class activation mapping (Grad-CAM) results showed correspondence between the high-response regions of the model and certain fracture-surface morphology regions.