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To reduce these risks, this study introduces a novel framework for detecting and classifying these unsafe operations for five types of construction machinery. Utilizing a cascade learning architecture, the approach employs a Super-Resolution Generative Adversarial Network (SRGAN), Real-Time Detection Transformers (RT-DETR), self-DIstillation with NO labels (DINOv2), and Dilated Neighborhood Attention Transformer (DiNAT) models. The study focuses on enhancing the detection and classification of unsafe operations in construction machinery through upscaling low-resolution surveillance footage and creating detailed high-resolution inputs for the RT-DETR model. This enhancement, by leveraging temporal information, significantly improves object detection and classification accuracy. The performance of the cascaded pipeline yielded an average detection and first-level classification precision of 96%, a second-level classification accuracy of 98.83%, and a third-level classification accuracy of 98.25%, among other metrics. 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