Personal Protective Equipment (PPE) detection is essential for strengthening intelligent surveillance systems and ensuring worker compliance with safety protocols in modern manufacturing facilities. A YOLO-based PPE detection model provides an efficient solution for resource-limited environments, supporting real-time operation at high speeds. This work proposes a Multi-Perspective Attention (MPA) module to enhance YOLO11n’s performance in PPE detection, enabling its feature extractors to focus on critical elements. Hence, the improved YOLO11n achieves superior results compared to other methods on the PPE Detection (PPED) and Color Helmet and Vest (CHV) datasets, operating at 19.94 and 28.09 frames per second (FPS) on an Intel Core i7-9750H CPU and NVIDIA Jetson Orin Nano GPU devices, respectively, without significantly increasing parameters or computational cost.