To address the issues of existing object detection models being prone to interference and resulting in insufficient accuracy when detecting barcodes in complex environments, as well as the high model complexity making deployment on low-computing-power mobile devices challenging, this study proposes a lightweight, high-precision detection algorithm called DOLN-YOLO based on YOLOv8. First, the DW-HGNetV2 architecture, reconstructed using deeply separable convolutions, is introduced as the backbone network, which enhances multiscale feature extraction capabilities while significantly reducing computational complexity. Second, the OD-C3Ghost module is constructed to replace the C2f module, enhancing dynamic perception capabilities for complex barcode deformations and further eliminating computational redundancy. Third, a lightweight shared detail enhancement detection head is designed, utilizing the gradient-strength dual-channel coordination mechanism of DEConv to enhance the model′s feature generalization capabilities, and adopts a heterogeneous convolution sharing strategy to reduce resource consumption; finally, a composite loss function NWD-PIoUV2 is proposed, combining normalized Wasserstein distance with dynamic focus PIoUV2 loss, to mitigate the optimization challenge of minor localization deviations and accelerate convergence speed. Experimental results demonstrate that, compared to the baseline model, DOLN-YOLO achieves a 0.92% improvement in mAP@0.5 and a 4.57% increase in mAP@0.5:0.95, while reducing parameters and computational costs by 58.8% and 48.6% respectively. This validates the algorithm′s superiority in detecting barcodes under complex environments. DOLN-YOLO provides a solution featuring both robust detection capability and efficient mobile deployment for logistics, healthcare, retail, and other application scenarios.