DCM-YOLO: Robust Electric Bicycle Detection in Confined Indoor Environments Under Occlusion and Image Degradation

To address electric bicycle detection in confined indoor environments affected by occlusion and image degradation, this study proposes DCM-YOLO, a robustness-oriented detection framework designed to improve detection accuracy under complex indoor visual conditions. First, the Dual-branch Adaptive Fusion (DAF) module combines lightweight feature generation with adaptive modulation to preserve local structures and channel diversity when target appearances are incomplete. Second, the Spatial–Channel Synergistic Attention (SCSA) mechanism sequentially refines informative regions and semantic channels, allowing the detector to suppress background interference more effectively. Third, the Multi-Scale Group-Aware Head (MSGA-Head) introduces multi-branch receptive-field modeling and grouped refinement to improve scale-sensitive classification and localization. These components form a coordinated backbone–attention–head design, reducing detection ambiguity caused by partial visibility and degraded image quality, including underexposure, overexposure, low contrast, and blur. Experimental results on a public dataset collected from representative indoor environments indicate that DCM-YOLO achieves 87.6% Precision, 83.7% Recall, 86.2% mAP50, and 65.1% mAP50-95, exceeding the baseline model by 2.5, 2.9, 2.8, and 1.7 percentage points, respectively. Additional evaluations on public benchmark datasets further verify the effectiveness and robustness of DCM-YOLO.