HCFF-Net: A Hybrid Contextual Feature Fusion Network for Robust Heavy Equipment Classification in Complex Construction Environments

Construction equipment plays a crucial role in performing essential tasks such as construction, demolition, and other maintenance. These machines enable workers to accomplish tasks that would otherwise be extremely difficult or impossible manually, significantly enhancing efficiency and productivity. Heavy construction equipment classification is a critical component of intelligent construction monitoring systems; however, existing vision-based methods often struggle under real-world conditions such as occlusion, background clutter, and scale variation. To address these challenges, this study proposes HCFF-Net, a hybrid contextual feature fusion network designed to enhance classification robustness in complex construction environments. The proposed framework integrates a diverse receptive residual fusion (DRRF) block to capture multi-scale local and global features and a global contextual channel recalibration (GCCR) module to adaptively refine channel-wise representations using contextual information. Unlike conventional feature fusion strategies, HCFF-Net effectively combines structural and contextual features to improve discriminative capability under challenging visual conditions. For performance evaluation, experiments were performed on the publicly available Alberta Construction Image Dataset (ACID). The proposed HCFF-Net achieves a classification accuracy of 90.60% and an F1-score of 90.05% across multiple equipment categories, outperforming state-of-the-art methods, validating its effectiveness for intelligent safety monitoring and management in construction environments.