改进YOLOv5的路侧单目视角小目标检测算法

Improved Roadside Monocular View Small Target Detection Algorithm Based on YOLOv5

针对车路协同感知交通场景下,路侧视角存在远距离目标及小目标识别精度较低、特征较少等问题,提出一种基于YOLOv5的小目标检测改进算法。首先,在主干网络中,加入GAM注意力模块增强网络特征提取能力;其次,引入RepBiPAN代替原颈部网络的PANet结构增加网络对小目标的定位能力;最后,使用SIoU损失函数代替原来的CIoU损失函数,能够有效地避免预测框在回归过程中的任意匹配,从而增强模型的鲁棒性,加快网络模型的训练速度。实验结果表明相比于原YOLOv5 6.0版本,当交并比IoU为0.5时各类平均精度mAP提升了6.9个百分点,当交并比IoU为0.95时各类平均精度mAP提升了6.4个百分点,有效地提高了路侧视角小目标检测的检测能力。

Aiming at the problems of low recognition accuracy and fewer features of long-distance targets and small targets in the roadside view under vehicle-road cooperative sensing traffic scenarios,an improved algorithm for small target detection based on YOLOv5 is proposed.Firstly,in the backbone network,the GAM attention module is added to enhance the feature extraction ability of the network.Secondly,RepBi-PAN is introduced to replace the PANet structure of the original neck network to increase the network’s ability to localize small targets.Finally,the use of SIoU loss function instead of the original CIoU loss function can effectively avoid the arbitrary matching of the prediction frames in the regression process,thus enhancing the robustness of the model and accelerating the training speed of the network model.The experimental results show that compared with the original YOLOv56.0 version,the average accuracy mAP of each category is improved by 6.9 percentage points when the intersection over union IoU is 0.5,and the average accuracy mAP of each category is improved by 6.4 percentage points when the intersection over union IoU is 0.95,which effectively improves the detection capability of small target detection in the road-side view.