Enhancing rotation you only look once version 8 for accurate detection of arbitrarily-oriented and multi-scale construction material in complex environment

Construction material detection is integral to effective material management. While deep learning-based methods have advanced automatic detection, challenges such as arbitrarily oriented objects, similar features, large object scale variations, and noise interference still limit accuracy. This study proposes an enhanced detection method based on improved rotation you only look once version 8, incorporating three key innovations. First, the large selective kernel network employs a spatial selection mechanism to dynamically adjust the network's receptive field, capturing key features and contextual information of various materials in complex construction scenarios. This enhances object detection performance in feature-similar environments. Second, the poly kernel inception network combines non-dilated multi-scale convolutions to extract dense texture features from differently sized objects, while using contextual anchor attention to capture long-range information for small objects. These components work together to improve multi-scale object detection. Third, the rectangular self-calibration module uses horizontal and vertical pooling to model rectangular attention regions, capturing axial global context. Its shape self-calibration function adjusts these regions to better fit arbitrarily oriented construction materials, enhancing focus on objects while suppressing noise interference. Experimental results show a mean average precision of 0.871–2.7 % higher than the baseline model and surpassing other state-of-the-art methods—while maintaining real-time detection speed at 28.3 frames per second. The proposed method improves material detection accuracy in complex construction environments, enabling refined material management on-site. This supports material entry and exit tracking, on-site usage monitoring, inventory management, and procurement planning, while also strengthening lean control of construction progress and costs.