【摘要】 针对自动驾驶场景下单阶段目标检测对小目标精度不足的问题，权衡精度与速度的共同需求，提出一种改进的YOLOv4目标检测算法。首先，在网络的残差模块中嵌入SimAM注意力模块，旨在提高网络对重要特征的提取能力，然后，利用ACON-C激活函数替换残差模块中的Mish激活函数，使残差模块可以自适应地激活，进而提升网络性能。在KITTI数据集上进行训练和测试，实验结果 表明，该模型的平均精度均值达到91.16%，检测速度达到32帧/s，满足实时检测的要求。更多还原

【Abstract】 Aiming at the problem of insufficient precision for small targets in single-stage target detection in autonomous driving scenarios,and weighing the common requirements of accuracy and speed,an improved YOLOv4target detection algorithm is proposed.First,the SimAM attention module is embedded in the residual module of the network to improve the network’s ability to extract important features,and then the ACON-C activation function is used to replace the Mish activation function in the residual module,so that the residual module can adaptively activation,thereby improving the performance of the network.Training and testing are performed on the KITTI data set.The experimental results show that the average precision of the model reaches 91.16%,and the detection speed reaches 32FPS,which meets the requirements of real-time detection.更多还原