引入视觉机制的SSD网络在摩托车头盔佩戴检测中的应用

近年来摩托车驾驶的安全问题越来越受到关注,摩托车手头盔的佩戴对自身的安全十分重要。针对于摩托车手是否佩戴头盔的检测问题,为提高检测网络的准确率和鲁棒性,对主流的一步检测网络SSD-Net引入类似视觉机制的模块,对网络的特征图在通道和空间上进行了权重的重新选择。并增加了类似人类视觉偏心率机制的RFB模块。通过使用Mosaic方法进行数据增强,并采用余弦衰减学习率来优化网络。实验结果表明改进后的网络对摩托车头盔佩戴的检测结果比原始SSD-Net提升了4%的MAP,具有更好的应用效果。

Application of SSD network with visual mechanism in motorcycle helmet wearing detection

In recent years, more and more attention has been paid to the safety of motorcyclists. Wearing helmets is very important for their own safety. In order to improve the accuracy and robustness of the detection network, in this paper, the mainstream one step detection network SSD net is introduced with similar visual mechanism module, and the weight of network feature map is reselected in channel and space. The RFB module is also added to the network, which is similar to the human visual eccentricity mechanism. We also use Mosaic method for data enhancement and cosine attenuation learning rate to optimize the network. The experimental results show that the MAP value of the improved network is about 4% higher than that of the original SSD net. And it has better application effect.