Current deep-learning-based excavator pose estimation methods usually face problems such as high memory consumption and low operation speed owing to large parameter redundancy. This paper presents a joint node-based excavator pose estimation approach using a lightweight fully convolutional network (FCN) that achieves higher accuracy with lower computation and storage requirements. The method directly encodes excavator joint nodes into multilevel features, and employs a deconvolution head to decode them into heat maps to provide joint node coordinates. The lightweight design is made at two levels: block level (employing depth-wise separable convolution instead of conventional convolution for efficiency) and layer level (employing the slimming technique to optimize layer channels for redundant depth removal). Using images collected from real construction sites, the superiority of the method was validated by comparing it with other state-of-the-art algorithms using various hardware platforms. The results indicate the high potential of excavator pose estimation for edge device deployment.

目前基于深度学习的挖掘机位姿估计方法由于参数冗余大，通常面临内存消耗大、运算速度慢等问题。本文提出了一种基于联合节点的挖掘机姿态估计方法，该方法使用轻量级全卷积网络 (FCN)，以更低的计算和存储要求实现更高的精度。该方法直接将挖掘机关节节点编码为多级特征，并采用反卷积头将其解码为热图以提供关节节点坐标。轻量级设计分为两个级别：块级（使用深度可分离卷积而不是传统卷积以提高效率）和层级（使用瘦身技术优化层通道以去除冗余深度）。使用从真实建筑工地收集的图像，通过将其与使用各种硬件平台的其他最先进的算法进行比较，验证了该方法的优越性。结果表明挖掘机位姿估计在边缘设备部署方面具有很高的潜力。