结合注意力机制和密集连接网络的车辆检测方法

Vehicle detection method combining attentionmechanism and dense connection network

为提高算法对车辆检测的准确性,解决原有算法在复杂交通场景下对车辆检测效果不佳的问题,提出一种基于注意力机制和改进密集连接网络结构的车辆检测方法。首先在过渡层中使用SoftPool整合密集块之间的特征信息;其次通过轻量化通道注意力机制加强有效通道特征的表达,将其作为Darknet-53的深层特征提取层;引入CIOU损失作为模型的边界框位置预测损失项,使用深度可分离卷积缩减模型体积;与原算法相比mAP值提高2.6%,模型体积缩减为原来的42%,实验证明本算法在复杂交通场景下具有良好的检测性能。

To improve the accuracy of the algorithm for vehicle detection and solve the problem that the original algorithm is not effective in the complex traffic scene,a vehicle detection method based on attention mechanism and improved densely connection network structure was proposed.Firstly,SoftPool was used in the transition layer to consolidate the characteristic information between the dense blocks.Secondly,the expression of effective channel features was enhanced by the lightweight channel attention mechanism,it was used as the deep feature extraction layer of Darknet-53.The CIOU loss was used as the prediction loss term of the bounding box position of the model,and reduce the model volume using deep separable convolution.Compared with the original algorithm,the mAP value is increased by 2.6%,and the model volume is reduced to 42%.Experimental results show that the algorithm has good detection performance in complex traffic scene.