To solve the problem of low accuracy in existing safety helmet detection methods, a novel object detection algorithm based on Single Shot Multibox Detector (SSD) is proposed in this paper. The algorithm uses the spatial attention mechanism for low-level features and the channel attention mechanism for high-level features, this cross-layer attention mechanism can further refine the feature information of the object region. The proposed detection algorithm introduces a feature pyramid and multiscale perception module to improve its robustness to object scale change. In addition, an effective anchor box adaptive adjustment method is designed to adaptively adjust the scale distribution of each layer of the anchor boxes based on the feature map size. Experiment results demonstrate that our detection model has mean Average Precision (mAP) of 88.1% and 80.5% on helmet dataset and VOC 2007 dataset respectively, which is better than baseline by 15.65% and 3.4%.

针对现有安全帽检测方法精度不高的问题，本文提出了一种基于单发多盒检测器（SSD）的目标检测算法。该算法对低层特征采用空间注意力机制，对高层特征采用通道注意力机制，这种跨层注意力机制可以进一步细化目标区域的特征信息。所提出的检测算法引入了特征金字塔和多尺度感知模块，以提高其对物体尺度变化的鲁棒性。此外，设计了一种有效的anchor box自适应调整方法，根据特征图大小自适应调整anchor box每层的尺度分布。实验结果表明，我们的检测模型的平均精度 (mAP) 为 88。