基于YOLO v4的车辆目标检测算法

针对车辆目标检测中存在遮挡目标导致检测精度低、小目标检测效果差等问题，提出一种基于YOLO v4改进的目标检测算法YOLO v4-ASC。通过在主干提取网络尾部加入卷积块注意力模块，提升网络模型的特征表达能力；改进损失函数提升网络模型的收敛速度，利用Adam+SGDM优化方法替代原始模型优化方法SGDM，进一步提升模型检测性能。此外，利用K-Means聚类算法优化先验框尺寸大小，并合并交通场景数据集中的car、truck、bus类别为vehicle，将本文问题简化为二分类问题。实验结果表明，本文提出的YOLO v4-ASC目标检测算法在保持原算法检测速度的基础上，AP达到了70.05%，F1-score达到了71%，与原YOLO v4算法相比，AP提升了9.92个百分点，F1-score提升了9个百分点。

Vehicle Target Detection Algorithm Based on YOLO v4

Aiming at the problems of low occlusion target detection accuracy and poor small target detection effect in vehicle target detection, an improved target detection algorithm YOLO v4-ASC based on YOLO v4 is proposed.  By adding convolution block attention module to the tail of the backbone extraction network, the feature expression ability of the network model is improved; The loss function is improved to improve the convergence speed of the network model, and the Adam+SGDM optimization method is used to replace the original model optimization method SGDM to further improve the model detection performance. In addition, K-Means clustering algorithm is used to optimize the priori frame size, and the car, truck and bus categories in the traffic scene data set are combined as vehicle, which simplifies the problem in this paper into a two classification problem. The experimental results show that on the basis of maintaining the detection speed of the original algorithm, the proposed YOLO v4-ASC target detection algorithm achieves 70.05% AP and 71% F1-score. Compared with the original YOLO v4 algorithm, AP is improved by 9.92 percentage points  and F1 score is improved by 9 percentage points.