Timely and accurate recognition of construction waste (CW) composition can provide yardstick information for its subsequent management (e.g., segregation, determining proper disposal destination). Increasingly, smart technologies such as computer vision (CV), robotics, and artificial intelligence (AI) are deployed to automate waste composition recognition. Existing studies focus on individual waste objects in well-controlled environments, but do not consider the complexity of the real-life scenarios. This research takes the challenges of the mixture and clutter nature of CW as a departure point and attempts to automate CW composition recognition by using CV technologies. Firstly, meticulous data collection, cleansing, and annotation efforts are made to create a high-quality CW dataset comprising 5,366 images. Then, a state-of-the-art CV semantic segmentation technique, DeepLabv3+, is introduced to develop a CW segmentation model. Finally, several training hyperparameters are tested via orthogonal experiments to calibrate the model performance. The proposed approach achieved a mean Intersection over Union (mIoU) of 0.56 in segmenting nine types of materials/objects with a time performance of 0.51 s per image. The approach was found to be robust to variation of illumination and vehicle types. The study contributes to the important problem of material composition recognition, formalizing a deep learning-based semantic segmentation approach for CW composition recognition in complex environments. It paves the way for better CW management, particularly in engaging robotics, in the future. The trained models are hosted on GitHub, based on which researchers can further finetune for their specific applications.

及时准确地识别建筑垃圾（CW）的成分可以为其后续管理（例如，隔离、确定适当的处置目的地）提供衡量标准信息。计算机视觉 (CV)、机器人技术和人工智能 (AI) 等智能技术越来越多地用于自动化废物成分识别。现有的研究侧重于控制良好的环境中的单个废物对象，但没有考虑现实生活场景的复杂性。本研究以 CW 的混合和杂波性质的挑战为出发点，并尝试使用 CV 技术实现 CW 成分识别的自动化。首先，进行细致的数据收集、清理和注释工作，以创建包含 5,366 张图像的高质量 CW 数据集。然后，引入了最先进的 CV 语义分割技术 DeepLabv3+ 来开发 CW 分割模型。最后，通过正交实验测试了几个训练超参数以校准模型性能。所提出的方法在分割九种类型的材料/对象时实现了 0.56 的平均交集交叉点 (mIoU)，每个图像的时间性能为 0.51 s。发现该方法对光照和车辆类型的变化具有鲁棒性。该研究有助于解决材料成分识别的重要问题，形成了一种基于深度学习的语义分割方法，用于复杂环境中的 CW 成分识别。它为未来更好的 CW 管理铺平了道路，尤其是在机器人技术方面。训练好的模型托管在 GitHub 上，