为解决轻量级目标检测算法中由于分类损失较大导致算法精确度低的问题, 提出一种对目标的位置与分类使用双检测头的检测方法. 算法中用卷积头对位置进行检测, 用全连接头对分类进行检测; 分类检测时特征图经过卷积层后融合位置回归分支的特征图, 再使用全连接层对特征图进行处理; 并提出分组全连接的方式进一步减少全连接层的计算量. 在VOC数据集上对算法进行训练, 结果表明, 改进后模型的分类损失有了明显的下降, 有效地提升了轻量级目标检测算法的检测精确度, 算法在VOC测试集上达到70.08%的精确度.
In order to solve the problem of low accuracy caused by large classification loss in the lightweight target detection algorithm, a method of detecting the location and classification of the target with double detection heads is proposed. In the algorithm, the convolution head is used to detect the position, and the full connector is used to detect the classification. In the classification detection, after the feature map passes through the convolution layer, the feature map of the fused position regression branch is processed through the full connection layer. A grouping full connection method is proposed to further reduce the amount of calculation in the full connection layer. The algorithm is trained in VOC datasets. The results show that the classification loss of the improved model is significantly reduced, and the detection accuracy of the lightweight target detection algorithm is effectively improved. The accuracy of the algorithm on the VOC test set has reached 70.08% mAP.