Lightweight Six-Class Personal Protective Equipment Detection in Construction Scenes Using YOLO11n

Abstract

Personal protective equipment (PPE) detection is a critical task in construction safety management, yet real-world construction scenes are often affected by occlusion, scale variation, complex backgrounds, and small-object interference. To evaluate the applicability of lightweight object detection for practical site monitoring, this study investigates a six-class PPE detection task in construction scenes based on YOLO11n, covering person, hard hat, vest, safety boots, gloves, and mask. Experiments were conducted on a public PPE dataset containing 1,617 images, which were divided into 1,100 training images, 304 validation images, and 213 test images. The model was trained with an input size of 640 × 640 for 50 epochs and evaluated using Precision, Recall, mAP@0.5, mAP@0.5:0.95, and inference speed. The results show that YOLO11n achieved Precision, Recall, mAP@0.5, and mAP@0.5:0.95 values of 0.821, 0.872, 0.846, and 0.566, respectively, on the test set, while maintaining an inference speed of approximately 103 FPS on an RTX 4050 Laptop GPU. Class-wise analysis indicates that person, hard hat, and vest were detected more reliably, whereas mask, gloves, and safety boots remained more challenging because of their small scale, occlusion sensitivity, and ambiguous visual features. Overall, the results demonstrate that lightweight YOLO11n provides a reasonable balance between detection accuracy and real-time performance for six-class PPE monitoring in construction environments, and it can serve as an effective baseline for future improvement in small-object enhancement and deployment-oriented optimization.