基于改进卷积神经网络的交通场景小目标检测

Small Object Detection in Traffic Scene Based on Improved Convolutional Neural Network

针对复杂交通场景中的小尺度车辆检测问题,提出改进的YOLOv3目标检测方法(S-YOLOv3)。使用ResNet网络优化YOLOv3的Darknet-53特征提取结构,采用特征金字塔网络获取目标的4个尺度特征以融合浅层特征和深层特征信息,并根据检测目标的大小调整损失函数的影响权重,从而增强小目标及相互遮挡物体的检测效果。在KITTI数据集上的实验结果表明,S-YOLOv3方法的检测速度和平均精度均值分别为52.45 frame/s和93.30%,相比YOLOv3方法在保证小目标检测实时性的同时具有更高的检测精度。

In view of the problems existing in the detection of small vehicles in complex traffic scenes,this paper proposes an improved objection detection method based on YOLOv3,S-YOLOv3.The method uses ResNet to improve the feature extraction structure of Darknet-53 of YOLOv3.The four scales of features of the object are obtained by using the Feature Pyramid Network(FPN)to fuse shallow feature information and deep feature information.Then the impact weight of the loss function is adjusted according to the size of the detection target,so as to enhance the detection performance of small objects and occluded objects.Experimental results on KITTI dataset show that the average detection speed of S-YOLOv3 is increased to 52.45 frame/s and Mean Average Precision(mPA)is increased to 93.30%.Compared with the YOLOv3 method,this proposed method can improve the precision of small object detection while ensuring the real-time performance.