A Cascaded Group Attention Mechanism-based Object Detection Algorithm for Construction and Demolition Waste

Accurate object detection is crucial for managing construction and demolition waste (CDW). However, existing deep-learning models often exhibit limited performance in detecting small objects within complex environments. This study proposes a YOLOv11-based detection algorithm integrated with a novel Cascaded Group Attention (CGA) mechanism to enhance the model’s ability to capture fine-grained features. First, we propose a transformer backbone based on CGA to improve long-range dependency modeling while substantially reducing redundant computations. Second, we employ a bidirectional multi-scale interaction module in the neck to integrate fine-grained details from high-resolution features with semantic context from low-resolution features, enabling accurate detection of CDW objects across scales. Finally, the proposed method is evaluated on two datasets. For comparison, we have reproduced several similar YOLOv11-based algorithms to validate the effectiveness of our approach. The results demonstrate a clear advantage of our approach, achieving mAP scores of 0.938 and 0.962, respectively, thereby surpassing the current state-of-the-art methods. Additionally, visualization of prediction results on test samples further confirms the high accuracy of our model.