Construction site monitoring relies on robust image classification to enhance safety, track progress, and optimize resource management. However, the amount of clutter and the high cost of manual labeling pose significant challenges. This paper presents an approach to multi-object classification in construction sites using Adaptive Active Transfer Learning. The Weighted Active Transfer Learning with Adaptive Sampling (WATLAS) framework is introduced, where Transfer Learning is combined with weighted Active Learning to efficiently classify diverse objects. A pre-trained InceptionV3 architecture integrated with bidirectional long short-term memory (BiLSTM) layers is utilized, and superior performance is achieved through adaptive sampling techniques compared to traditional methods. WATLAS achieves 97 % accuracy on a comprehensive dataset of 9344 construction site images spanning 15 object categories and maintaining 90 % accuracy with only 5 % labeled data. By optimizing performance metrics, the framework demonstrates significant improvements over traditional methods, making it a scalable solution for construction site monitoring.

中文翻译：

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用于建筑工地图像中不平衡多目标分类的加权自适应主动迁移学习

施工现场监控依靠强大的图像分类来提高安全性、跟踪进度并优化资源管理。然而，手动贴标的杂乱数量和高成本带来了重大挑战。本文提出了一种使用自适应主动迁移学习在建筑工地进行多对象分类的方法。引入了具有自适应采样的加权主动迁移学习 （WATLAS） 框架，其中迁移学习与加权主动学习相结合，以有效地对各种对象进行分类。利用与双向长短期记忆 （BiLSTM） 层集成的预训练 InceptionV3 架构，与传统方法相比，通过自适应采样技术实现了卓越的性能。WATLAS 在包含 9344 张建筑工地图像（涵盖 15 个对象类别）的综合数据集上实现了 97% 的准确率，并且仅对 5% 的标记数据保持了 90% 的准确率。通过优化性能指标，该框架与传统方法相比有了显著改进，使其成为建筑工地监控的可扩展解决方案。