轻量化CenterNet网络的二维条码定位算法

针对复杂工业、物流运输场景中传统的二维条码定位算法效率和稳定性较低的问题，提出了一种基于轻量化的CenterNet网络的二维条码定位算法。针对实际情况中二维条码尺寸变化问题，采用CSPDarkNet53-tiny作为主干网络并对其加以修改。添加SPP模块以提高网络精度，对CenterNet的上采样以及输出模块部分进行轻量化改造，使用5×5深度可分离卷积代替普通卷积，并在训练时采用余弦退火学习率策略防止过拟合。实验结果表明，在定位准确率仅比YOLOv4-tiny降低0.64%的情况下，不仅能够避免传统算法准确率受背景影响大、鲁棒性不强等问题，而且实时推理速度可以达到124 fps,可以更好的用于低硬件配置下各种二维条码定位。

Two-dimensional barcode positioning algorithm oflightweight CenterNet network

Aiming at the low efficiency and stability of the traditional two-dimensional bar code positioning algorithm in complex industrialand logistics transportation scenarios, a two-dimensional bar code positioning algorithm based on lightweight CenterNet network isproposed, a lightweight CenterNet detection algorithm is proposed. In view of the size change of two-dimensional bar code in the actualsituation, CSPDarknet53-tiny is used as the backbone network and modified SPP module is added to improve the accuracy of thenetwork. The upsampling and detection head of CenterNet are lightweight transformed. 5 × 5 depth separable convolution is used toreplace ordinary convolution. The change strategy of learning rate during training adopts cosine annealing learning rate to prevent overfitting. The experimental results show that the positioning accuracy is only 0. 64% lower than YOLOv4 tiny. It not only avoids theproblems that the accuracy of the traditional algorithm is greatly affected by the background and the robustness is not strong, the real-timereasoning speed also reaches 124 fps, which can be better used for all kinds of two-dimensional bar code location under low hardwareconfiguration.