Container-surface-guided dual-task learning for defect detection in complex port scenes

Automatic container defect inspection in complex port environments is hindered by substantial scale variation and defect-like interference from non-container regions. This article presents CSG-YOLO, a container-surface-guided dual-task framework that jointly learns five-class defect detection and container-surface segmentation. Its scale-tuned feature fusion module adapts multi-level neck features to defects of different spatial extents, while mask-guided cross-task interaction bidirectionally exchanges the learned surface prior and defect cues between the task heads. Center-to-container consistency and edge-aware mask refinement losses respectively encourage defect-center coverage and surface-boundary consistency. On a single-terminal dataset of 4,000 distinct port images containing 4,480 defect instances, CSG-YOLO-N achieves mean scores of 58.1 average precision (AP), 93.0 AP50, and 65.0 AP75 across three independent training runs, with 16.3 GFLOPs. Compared with YOLOv12-N, it improves these metrics by 2.4, 3.2, and 4.1 points, respectively, while reducing mean overall missed detection rate from 6.4% to 5.7%, false positives per image from 0.19 to 0.11, and outside-region-of-interest (ROI) detection ratio from 1.8% to 0.2%. It also obtains a mean Surface intersection over union of 76.7%. These results support the effectiveness of explicit container-surface modeling for defect detection and suppression of spatially invalid detections on the evaluated dataset. Generalization to other port environments remains to be established.