地面箭头标识线检测的改进M2Det算法

针对M2Det算法在地面箭头标识线检测时存在准确率低、参量大等问题，提出了一种改进M2Det算法。该算法在特征提取时采用改进的主干特征提取网络和多级金字塔网络，利用非极大抑制对生成的密集边界框和类别分数进行筛选，进而获得检测结果。改进的M2Det算法用MobileNet v1轻量级网络替换VGG网络，用以减少参量；用Mish激活函数替换ReLU激活函数，同时在MobileNet v1网络中增加BasicRFB模块，用以提高检测精度；还引入Mosaic数据增强以实现数据扩充。实验数据集采用自主标注的地面箭头标识线构造，实验结果表明，改进的M2Det算法在地面箭头标识线检测中mAP达到88.72%，相比M2Det算法提升了约3.9个百分点，也明显高于其它对比算法。

An improved M2Det algorithm forground arrow marking line detection

An improved M2Det detection algorithm is proposed to solve the problems of low accuracy and large amount of parameters in the detection of ground arrow marking lines. The algorithm uses an improved backbone feature extraction network and a multi-level pyramid network in feature extraction, and uses non-maximum suppression to filter the generated dense bounding boxes and class scores to obtain detection results. Lightweight network named MobileNet v1 is adopted to replace the VGG network in order to reduce the number of parameters. Mish activation function is used to substitute the ReLU activation function. Meanwhile, BasicRFB module is added to the MobileNet v1 network to increase the detection accuracy. Mosaic data augmentation is also introduced to enable data augmentation. Self- labeled ground arrow lines are used as the experimental dataset, and the experimental results show that the mAP of the improved M2Det algorithm achieves 88.72%, which is about 3.9% higher than the mAP of the original M2Det algorithm, and significantly higher than the mAP of other comparison algorithms.