Abstract
This paper presents a deep learning-based framework for classifying forestry operations from dashcam video footage. Focusing on four key work elements - crane-out, cutting-and-to-processing, driving, and processing - the approach employs a 3D ResNet-50 architecture implemented with PyTorchVideo. Trained on a manually annotated dataset of field recordings, the model achieves strong performance, with a validation F1 score of 0.88 and precision of 0.90. These results underscore the effectiveness of spatiotemporal convolutional networks for capturing both motion patterns and appearance in real-world forestry environments. The system integrates standard preprocessing and augmentation techniques to improve generalization, but overfitting is evident, highlighting the need for more training data and better class balance. Despite these challenges, the method demonstrates clear potential for reducing the manual workload associated with traditional time studies, offering a scalable solution for operational monitoring and efficiency analysis in forestry. This work contributes to the growing application of AI in natural resource management and sets the foundation for future systems capable of real-time activity recognition in forest machinery. Planned improvements include dataset expansion, enhanced regularization, and deployment trials on embedded systems for in-field use.
Abstract (translated)
本文提出了一种基于深度学习的框架，用于从行车记录仪视频片段中分类林业作业。该方法重点关注四个关键工作元素——起重机操作、砍伐和运输、驾驶以及加工，并采用了PyTorchVideo实现的3D ResNet-50架构。模型在手动标注的实地录音数据集上进行训练，在验证集上的F1评分为0.88，精确度为0.90。这些结果突显了时空卷积网络在捕捉现实世界林业环境中运动模式和外观方面的有效性。 该系统整合了标准的数据预处理和增强技术以提高泛化能力，但过拟合问题明显存在，这表明需要更多的训练数据和更好的类别平衡。尽管面临挑战，该方法展示了减少传统时间研究中的手动工作量的明确潜力，并为林业作业监控和效率分析提供了一种可扩展解决方案。 这项工作促进了人工智能在自然资源管理领域的应用，并为未来实时活动识别森林机械设备奠定了基础。计划改进包括扩大数据集、增强正则化以及在嵌入式系统上进行部署试验以供现场使用。
URL
https://arxiv.org/abs/2505.24375
