基于注意力机制和跨尺度特征融合的摩托车头盔检测算法

Motorcycle helmet detection algorithm based on attention mechanism and cross-scale feature fusion

在道路交通摩托车事故中,未佩戴头盔是导致骑乘人员受到致命伤害的主要原因。针对目前头盔检测中由于黑色头发、帽子和头盔的颜色和形状相似存在误检和漏检问题,提出了一种具有三重注意力机制和双向跨尺度特征融合的摩托车头盔检测算法。首先,在YOLOV5s的主干网络中引入三重注意力机制,提取了不同维度之间的语义依赖,消除了通道和权重的间接对应关系,通过关注相似样本的差异从而提升检测精度。其次,采用EIOU边框损失函数优化对遮挡和重叠目标的检测效果。最后,在特征金字塔中采用加权双向特征金字塔网络结构,实现高效的双向跨尺度连接和加权特征融合,增强了网络特征提取能力。实验结果表明,改进算法实现了98.7%的mAP@0.5、94.0%的mAP@0.5:0.95,与原算法相比,改进算法的mAP@0.5提升了3.9%以及mAP@0.5:0.95提升了7.6%,具有更高的精度和更强的泛化能力。

In road traffic motorcycle accidents,failure to wear a helmet is the leading cause of fatal injuries to riders.Aiming at the problems of false detection and missed detection in the current helmet detection due to the similarity in color and shape of black hair,hat and helmet,a motorcycle helmet detection algorithm with triplet attention mechanism and bidirectional cross-scale feature fusion is proposed.First,a triplet attention mechanism is introduced into the backbone network of YOLOV5s,which extracts semantic dependencies between different dimensions,eliminates the indirect correspondence between channels and weights,and improves detection accuracy by paying attention to the differences between similar samples.Second,the EIOU bounding loss function is used to optimize the detection effect of occluded and overlapping objects.Finally,the weighted bidirectional feature pyramid network structure is adopted in the feature pyramid to achieve efficient bidirectional cross-scale connection and weighted feature fusion,which enhances the network feature extraction capability.The experimental results show that the improved algorithm achieves 98.7% mAP@0.5 and 94.0% mAP@0.5:0.95.Compared with the original algorithm,the improved algorithm′s mAP@0.5 increases by 3.9% and mAP@0.5:0.95 increases by 7.6%,with higher accuracy and stronger generalization ability.