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As reconstruction and redevelopment accelerate, the generation of construction waste increases, and construction waste treatment technology is being developed accordingly, especially using artificial intelligence (AI). The majority of AI research projects fail as a consequence of poor learning data as opposed to the structure of the AI model. If data pre-processing and labeling, i.e., the processes prior to the training step, are not carried out with development purposes in mind, the desired AI model cannot be obtained. Therefore, in this study, the performance differences of the construction waste recognition model, after data pre-processing and labeling by individuals with different degrees of expertise, were analyzed with the goal of distinguishing construction waste accurately and increasing the recycling rate. According to the experimental results, it was shown that the mean average precision (mAP) of the AI model that trained on the dataset labeled by non-professionals was superior to that labeled by professionals, being 21.75 higher in the box and 26.47 in the mask, on average. This was because it was labeled using a similar method as the Microsoft Common Objects in Context (MS COCO) datasets used for You Only Look at Coefficients (YOLACT), despite them possessing different traits for construction waste. Construction waste is differentiated by texture and color; thus, we augmented the dataset by adding noise (texture) and changing the color to consider these traits. This resulted in a meaningful accuracy being achieved in 25 epochs—two fewer than the unreinforced dataset. In order to develop an AI model that recognizes construction waste, which is an atypical object, it is necessary to develop an explainable AI model, such as a reconstruction AI network, using the model’s feature map or by creating a dataset with weights added to the texture and color of the construction waste.

Comparison of the Performance of Artificial Intelligence Models Depending on the Labelled Image by Different User Levels