A Visual Detection and Multi-Zone Personnel Safety Control Method for Firework Manufacturing Workshops

Real-time visual detection is essential for personnel safety control in firework manufacturing workshops, where inadequate personnel-count control can increase safety risks in hazardous production zones. This study proposes a visual detection and multi-zone personnel safety control method that combines edge-oriented personnel detection, cross-camera identity association, and polygon-based boundary filtering. The detection module is built on YOLO26, which supports inference without non-maximum suppression (NMS). A Global Attention Mechanism (GAM) is adopted instead of the Convolutional Block Attention Module (CBAM) because its sequential channel-spatial attention preserves cross-dimensional interactions without the global pooling operations used in CBAM, thereby retaining weak spatial cues from small personnel targets. GAM is incorporated after the Spatial Pyramid Pooling-Fast (SPPF) module to improve detection under overhead views, dust, and occlusion. A cross-camera person re-identification (ReID) layer maintains identity consistency across workshops, while an Irregular Electronic Fence (IEF) excludes detections outside hazardous operating boundaries. On in-situ data collected from Deren Firework Co., Ltd., the proposed method achieves a 98.3% mean average precision at an intersection-over-union threshold of 0.5 (mAP@0.5) with a per-image central processing unit (CPU) processing time of 36.5 ms. Compared with YOLOv8n, this represents a 5.9 percentage point improvement in mAP@0.5 and a 54.6% latency reduction. A three-month field deployment detected 15 safety breaches and supported timely intervention in 4 critical overcrowding incidents, indicating the practical applicability of the method under the evaluated factory conditions.