基于改进CenterNet的托盘定位方法

目前, 托盘定位大多采用基于深度神经网络的目标检测算法, 一般使用矩形框进行托盘定位, 托盘中心点定位精度不高, 且无法有效估计托盘水平方向. 针对此问题, 本文提出了基于关键点检测的托盘定位方法, 通过检测托盘正面外轮廓的4个角点来定位托盘. 首先, 由于目前没有大规模的托盘数据集, 使用迁移学习的方法, 将CenterNet的人体姿态估计引入托盘定位任务. 然后改进关键点分组方法, 并提出关键点回归自适应补偿, 提高关键点检测精度. 在托盘关键点定位的基础上, 提出基于几何约束的托盘中心点计算和托盘水平方向估计方法. 本文方法与原CenterNet相比, 托盘关键点定位指标${{A}}{{{P}}^{{\text{kp}}}}$从0.352提高到0.728, 托盘中心点定位精度指标${{ALP}}$达到0.946, 并且可以有效估计托盘水平方向, 具有较高的实用价值.

Pallet Positioning Method Based on Improved CenterNet

Currently, the target detection algorithm based on depth neural network is mostly used for pallet positioning and a rectangular box is generally utilized. The positioning accuracy of the pallet center point is not high enough, and the horizontal direction of the pallet cannot be estimated effectively. To solve this problem, this study proposes a pallet positioning method based on keypoint detection, which locates the pallet by detecting the four corners of the front outer outline. Firstly, due to the shortage of large-scale pallet data sets, the human posture estimation of CenterNet is introduced by transfer learning. Then the keypoint grouping method is improved, and the adaptive compensation is proposed for keypoint regression to improve the keypoint detection accuracy. According to the location of pallet keypoints, a method of pallet center point calculation and pallet horizontal direction estimation based on geometric constraints is proposed. Compared with the original CenterNet, the proposed method raises the positioning index AP^kp of pallet keypoint from 0.352 to 0.728, and the positioning accuracy ALP of pallet center point to 0.946. Meanwhile, it can effectively estimate the pallet horizontal direction and is of high practical value.