In the steel industry， carbide is a vital constituent， whose distribution in steel materials holds significant reference value for evaluating steel quality. However， the current detection methods for carbide in steel bars primarily rely on manual inspection， which is costly and lacks stability. This study introduces advanced deep learning techniques from the domain of artificial intelligence， which collects and annotates 3 192 high quality images of banded carbides on steel bars， alongside 11 complete samples to create a banded carbide dataset on object detection for steel bars （BCDOD）. Common deep learning methods for object detection are applied to the dataset through experimental analysis. With a focus on the specific characteristics of the application scenario and data， the cascade R-CNN model is enhanced with rotation data augmentation， improvement to the Focal Loss function and negative sample fine-tuning， resulting in performance improvement. The achieved average precision reaches 96%， with 100% recognition accuracy on complete sample data， showcasing promising outcomes that address the existing gap in artificial intelligence technology within the field of carbide metallographic detection.