MAS-HENet: A Factory Safety Detection Network Integrating Feature Aggregation and High-Efficiency Up-Sampling Convolution

To address the demand for high-precision multi-object real-time detection in factory safety production scenarios, this paper proposes an improved real-time visual detection model. The model is built upon YOLOv8 and introduces the MAStar module to replace part of the C2f structure, enhancing feature reuse and gradient flow. A small-object detection layer is added to improve the perception of small-scale targets, while a lightweight shared detection head is adopted to reduce the number of parameters. Furthermore, a High-Efficiency Up-sampling Convolution (HEUC) module is designed to achieve more accurate feature reconstruction. Experimental results show that, on the self-built dataset, the proposed model improves mAP@50 and mAP@50:95 by 3.8 percentage points compared with the original model, and by 6.6 percentage points on the public VisDrone2019 dataset. The proposed method achieves a favorable balance between detection accuracy and computational efficiency, providing an effective solution for multi-object real-time detection in industrial safety monitoring.