To address the issue of missed and false detections of small objects of helmet wearing detection in construction scenarios， caused by reasons such as crowding， occlusion， and complex backgrounds， a cross-layer multi-scale helmet wearing detection algorithm with double attention mechanism based on YOLOv8n was proposed. Firstly， a small object detection head was designed to enhance the model’s ability to detect small objects. Secondly， the double attention mechanism was embedded in the feature extraction network to focus more on capturing object features in complex scenarios. Thirdly， the feature fusion network was replaced with the cross-layer multi-scale feature fusion structure S-GFPN （Selective layer Generalized Feature Pyramid Network）， which was improved with Re-parameterized Generalized Feature Pyramid Network （RepGFPN）， so as to enable multi-scale fusion of small object feature layer with other layers and establish long-term dependencies， thus reducing background information interference. Finally， the MPDIOU （Intersection Over Union with Minimum Point Distance） loss function was employed to address non-sensitivity issues related to scale changes. Experimental results on the public dataset GDUT-HWD show that compared to the YOLOv8n， the improved model increases the mAP@0.5 by 3.4 percentage points， and improves the detection accuracy for blue， yellow， white， and red helmets by 2.0， 1.1， 4.6， and 9.1 percentage points， respectively. The model also outperforms the YOLOv8n in five complex scenarios： density， occlusion， small objects， light reflection， and darkness， and provides an effective method for helmet wearing detection in real-world construction scenarios.