Steel containers are important carriers for packaging, storage, and transportation, and their surface quality directly affects product quality and service performance. To address the complex morphology and distinct directional characteristics of steel surface defects, as well as the insufficient utilization of directional features by existing detection models, the work aims to propose a Mamba-YOLO-based visual detection method for steel container surface defects. The multi-directional feature fusion in the SS2D module of Mamba-YOLO was improved. Firstly, a fixed directional prior was constructed from the directional distribution of defect targets in the training set and learnable correction parameters were introduced. Secondly, combined with the adaptive directional gating mechanism, the fusion weights of different scanning directions were adjusted according to the input features, thereby forming the Mamba-YOLO-GatePrior model. The model was trained and tested on the NEU-DET and GC10-DET datasets and compared with target detection models such as YOLOv8n, YOLO11n, and YOLO26n. Experimental results showed that Mamba-YOLO-GatePrior achieved mAP50 values of 76.72% and 74.98% on NEU-DET and GC10-DET, respectively, representing improvements of 2.05 and 2.29 percentage points over the Mamba-YOLO baseline. Ablation and comparative experiments further demonstrated that the model achieved favorable overall detection performance across different defect types while maintaining satisfactory inference efficiency. The proposed surface defect detection method for steel containers improves defect recognition in complex industrial scenarios and achieves a favorable balance between detection accuracy and inference efficiency, providing a technical reference for intelligent surface defect inspection of steel containers and quality control of packaging containers.
Key words
steel container /
surface defect detection /
visual detection /
Mamba-YOLO
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