GAM-YOLOv8n: enhanced feature extraction and difficult example learning for site distribution box door status detection.

The detection of distribution box doors on construction sites is particularly important in site safety management, but the size and posture of distribution boxes vary in different scenarios, and there are still challenges. This article proposes an improved YOLOv8n construction site distribution box door status detection and recognition method. Firstly, Global Attention Mechanism is introduced to reduce information dispersion and enhance global interaction representation, preserving the correlation between spatial and channel information to strengthen the network's feature extraction capability during the detection process. Secondly, to tackle the problem of class imbalance in construction site distribution box door state detection, the Focal_EIoU detection box loss function is used to replace the CIoU loss function, optimizing the model's ability to learn from difficult samples.Lastly,the proposed method is evaluated on a dataset of distribution boxes with different shapes and sizes collected from various construction scenes. Experimental results demonstrate that the improved YOLOv8n algorithm achieves an average precision (mAP) of 82.1% at a speed of 66.7 frames per second, outperforming other classical object detection networks and the original network. This improved method provides an efficient and accurate solution for practical detection tasks in smart chemical sites, especially in enhancing feature extraction and processing difficult sample cases, which has made significant progress. [ABSTRACT FROM AUTHOR]