Rock debris recognition is an important tool in geological exploration and logging. To improve the efficiency of traditional manual lithology identification and overcome the challenges of slow inference and high computational complexity in common deep learning networks, this study proposes DAF-STDC, a real-time semantic segmentation network for rock debris images based on a well-performing STDC network model. The network uses dilated convolution to maintain resolution while extracting features and utilizes an attention mechanism to help the model acquire global information from the feature map, thus refining the edge information of rock debris particles. It also uses a feature fusion module to enhance the fusion of low-level detail features and high-level semantic features, improving feature representation. Experiments have proved that the improved network model significantly enhances accuracy. The mean intersection over union of DAF-STDC reaches 83.12% on the RC_Dataset which consists of six types of rock debris images collected from exploratory wells. While maintaining the number of parameters, DAF-STDC significantly improves its inference speed and segmentation accuracy, providing an effective reference for the digitization of rock debris logging.