改进多尺度网络的行人目标检测算法

针对自动驾驶情景下行人目标检测过程中对于重叠和遮挡目标存在的漏检问题，提出一种改进多尺度网络YOLOv5的行人目标检测算法。首先构建同时考虑通道间关系和特征空间位置信息的多重协调注意力模块，增加网络特征表达能力；然后将原损失函数改进为具有双重惩罚项的切比雪夫距离交并比损失函数，提高检测框的精确度与网络收敛速度；最后在网络结构方面设计瓶颈状DSP1_X和DSP2_X模块减少梯度混淆。实验结果表明，改进后的多尺度网络收敛能力提高，在面对行车中复杂行人目标检测时具有较高的判别精度和实时检测速度。

Pedestrian target detection algorithm based on improved multi-scale network

Aiming at the missed detection of overlapping and occluded targets in the process of pedestrian target detection in autonomous driving scenarios, a pedestrian target detection algorithm based on improved multi-scale network YOLOv5 is proposed. Firstly, construct a multiple coordinated attention module that considers the relationship between channels and the location information of the feature space at the same time to increase the ability of network feature expression. Then the original loss function is improved to the Chebyshev distance intersection and ratio loss function with double penalty terms to improve the detection frame The accuracy of the network and the speed of network convergence. Finally, the bottleneck-shaped DSP1_X and DSP2_X modules are designed in the network structure to reduce gradient confusion. The experimental results show that the improved multi-scale network has improved convergence ability, and has higher discrimination accuracy and real-time detection speed when facing complex pedestrian target detection in driving.