Cui Yunhao, Zhang Zhihui, An Yi, Zhong Zhidan, Yang Fang, Wang Junhua, He Kui
School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang 471023, China.
School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China.
Sensors (Basel). 2024 Jul 5;24(13):4364. doi: 10.3390/s24134364.
The semantic segmentation of the 3D operating environment represents the key to intelligent mining shovels' autonomous digging and loading operation. However, the complexity of the operating environment of intelligent mining shovels presents challenges, including the variety of scene targets and the uneven number of samples. This results in low accuracy of 3D semantic segmentation and reduces the autonomous operation accuracy of the intelligent mine shovels. To solve these issues, this paper proposes a 3D point cloud semantic segmentation network based on memory enhancement and lightweight attention mechanisms. This model addresses the challenges of an uneven number of sampled scene targets, insufficient extraction of key features to reduce the semantic segmentation accuracy, and an insufficient lightweight level of the model to reduce deployment capability. Firstly, we investigate the memory enhancement learning mechanism, establishing a memory module for key semantic features of the targets. Furthermore, we address the issue of forgetting non-dominant target point cloud features caused by the unbalanced number of samples and enhance the semantic segmentation accuracy. Subsequently, the channel attention mechanism is studied. An attention module based on the statistical characteristics of the channel is established. The adequacy of the expression of the key features is improved by adjusting the weights of the features. This is done in order to improve the accuracy of semantic segmentation further. Finally, the lightweight mechanism is studied by adopting the deep separable convolution instead of conventional convolution to reduce the number of model parameters. Experiments demonstrate that the proposed method can improve the accuracy of semantic segmentation in the 3D scene and reduce the model's complexity. Semantic segmentation accuracy is improved by 7.15% on average compared with the experimental control methods, which contributes to the improvement of autonomous operation accuracy and safety of intelligent mining shovels.
三维作业环境的语义分割是智能矿用挖掘机自主挖掘和装载作业的关键。然而，智能矿用挖掘机作业环境的复杂性带来了挑战，包括场景目标的多样性和样本数量不均衡。这导致三维语义分割的准确率较低，降低了智能矿用挖掘机的自主作业精度。为了解决这些问题，本文提出了一种基于记忆增强和轻量级注意力机制的三维点云语义分割网络。该模型解决了采样场景目标数量不均衡、关键特征提取不足导致语义分割精度降低以及模型轻量级程度不足导致部署能力下降等挑战。首先，我们研究了记忆增强学习机制，为目标的关键语义特征建立了一个记忆模块。此外，我们解决了由于样本数量不均衡导致的非主导目标点云特征遗忘问题，并提高了语义分割精度。随后，研究了通道注意力机制。建立了一个基于通道统计特征的注意力模块。通过调整特征权重来提高关键特征表达的充分性。这样做是为了进一步提高语义分割的准确率。最后，通过采用深度可分离卷积代替传统卷积来研究轻量级机制，以减少模型参数数量。实验表明，所提出的方法可以提高三维场景中语义分割的准确率，并降低模型的复杂度。与实验对照方法相比，语义分割准确率平均提高了7.15%，这有助于提高智能矿用挖掘机的自主作业精度和安全性。
