【摘要】 针对夜间车辆检测模型的精度要求，提出以夜间车辆为研究对象，利用深度学习中的卷积神经网络构建检测模型。首先对数据集进行白平衡处理以减少路灯颜色的干扰进而增强图像画质，并用Mosaic数据增强来丰富检测数据集进而提升模型对小目标车辆的检测效果；其次针对先验框的选取采用K-means++算法，并利用交并比距离对先验框进行聚类；接着向主干特征提取网络加入注意力机制模块来增强残差结构特征图中目标的通道和空间特征信息；最后在损失函数的原始置信度交叉熵损失中引入梯度均衡机制，使模型有效衰减难易样本。通过在UA-DETRAC数据集的实验与对比分析可知：本文提出的夜间车辆检测算法的精度可达99.24%，同时每秒处理图像帧数高达19帧，验证了该算法的有效可行性。更多还原

【Abstract】 Aiming at the accuracy requirements of the night vehicle detection model, this paper proposes the night vehicle as the reasearch object and uses convolution neural network in deep learning to construct the detection model. Firstly, the data set is processed with white balance to reduce the interference of street lamp color and enhance the image quality, and mosaic data enhancement is used to enrich the detection data set and improve the detection effect of the model for small target vehicles; Secondly, K-means ++ algorithm is used to select the prior box, and the intersection and union ratio distance is used to cluster the prior box; Then the attention mechanism module is added to the backbone feature extraction network to enhance the channel and spatial feature information of the target in the residual structure feature map; Finally, the gradient equilibrium mechanism is introduced into the original confidence cross entropy loss of the loss function to make the model attenuate the hard and easy samples effectively. Through the experiments and comparative analysis on UA-DETRAC data set, it can be seen that the accuracy of the proposed algorithm can reach 99. 24%, and the number of frames per second to process image can reach 19.更多还原