LSH-YOLO: A Lightweight Algorithm for Helmet-Wear Detection

This work addresses the high computational cost and excessive parameter count associated with existing helmet-wearing detection models in complex construction scenarios. This paper proposes a lightweight helmet detection model, LSH-YOLO (Lightweight Safety Helmet) based on improvements to YOLOv8. First, the KernelWarehouse (KW) dynamic convolution is introduced to replace the standard convolution in the backbone and bottleneck structures. KW dynamically adjusts convolution kernels based on input features, thereby enhancing feature extraction and reducing redundant computation. Based on this, an improved C2f-KW module is proposed to further strengthen feature representation and lower computational complexity. Second, a lightweight detection head, SCDH (Shared Convolutional Detection Head), is designed to replace the original YOLOv8 Detect head. This modification maintains detection accuracy while further reducing both computational cost and parameter count. Finally, the Wise-IoU loss function is introduced to further enhance detection accuracy. Experimental results show that LSH-YOLO increases mAP50 by 0.6%, reaching 92.9%, while reducing computational cost by 63% and parameter count by 19%. Compared to YOLOv8n, LSH-YOLO demonstrates clear advantages in computational efficiency and detection performance, significantly lowering hardware resource requirements. These improvements make the model highly suitable for deployment in resource-constrained environments for real-time intelligent monitoring, thereby advancing the fields of industrial edge computing and intelligent safety surveillance.