Efficiency comparative study.

Focusing on the practical challenges of insufficient samples, incomplete categories, and low detection accuracy (particularly for small targets) in Personal Protective Equipment (PPE) wearing condition monitoring for operators in offshore environments, this research investigates PPE targets detection for offshore operators using an improved YOLOv11 model. The optimized model integrates the time-frequency features enhancement module (Spatial Pyramid Pooling-Fast, SFEAF) into the model’s backbone network, employs a statistical-driven dynamic gating attention module (Token Statistics Self-Attention, TSSA) to refine attention weight distribution in the original C2PSA module, and incorporates a Normalized Wasserstein Distance (NWD) loss function. These modifications collectively enhance the model’s capability to detect PPE targets for offshore operators. To mitigate missed detection problem of small targets such as earplugs and gloves, a cascaded network of YOLOv11 and YOLOv11-Pose models is proposed for small targets detection. The solution involves extracting human key points through YOLOv11-Pose model, constructing spatial constraint regions via two-point area positioning method, enhancing small target features through localized region cropping and normalization, and performing secondary detection on refined regions using YOLOv11 model. The ablation experiments show that the mAP@0.5 value of the optimization model has been improved by 1.8 percentage points compared to the original model for all targets, and the precision rates for both positive and negative samples of small targets—earplugs and gloves—are respectively improved by 5.2%, 4.2%, 0.2%, and 3.7%. The superiority of the optimization method has been proved. Furthermore, secondary detection experiments on small targets yielded an average Missed Detection Recovery Rate (MRR) of 56.64%, and the effectiveness of the multi-model cascaded detection method has been verified.

针对近海环境作业人员个人防护装备（Personal Protective Equipment，PPE）佩戴状态监测中存在的样本不足、类别不全、检测精度偏低（尤其针对小目标）等实际挑战，本研究基于改进型YOLOv11模型开展近海作业人员PPE目标检测研究。该优化模型将时频特征增强模块（Spatial Pyramid Pooling-Fast，SFEAF）集成至模型骨干网络，采用统计驱动动态门控注意力模块（Token Statistics Self-Attention，TSSA）对原始C2PSA模块的注意力权重分布进行精细化调整，并引入归一化瓦瑟斯坦距离（Normalized Wasserstein Distance，NWD）损失函数。上述多项改进共同提升了模型对近海作业人员PPE目标的检测能力。为缓解耳塞、手套等小目标的漏检问题，本研究提出YOLOv11与YOLOv11-Pose级联网络用于小目标检测。该方案通过YOLOv11-Pose模型提取人体关键点，借助两点区域定位法构建空间约束区域，通过局部区域裁剪与归一化强化小目标特征，并利用YOLOv11模型对精细化后的区域开展二次检测。消融实验结果表明，相较于原始模型，优化后模型的全目标mAP@0.5值提升了1.8个百分点；针对耳塞、手套两类小目标的正、负样本精确率分别提升5.2%、4.2%、0.2%与3.7%，证实了所提优化方法的优越性。此外，针对小目标的二次检测实验平均漏检恢复率（Missed Detection Recovery Rate，MRR）可达56.64%，验证了多模型级联检测方法的有效性。