改进SSD算法的道路小目标检测研究

在道路场景中，因小目标分辨率低且特征不明显，传统的目标检测算法难以确认其所属类别和位置信息，导致检测精度低、检测速度慢、漏检率高。提出一种改进SSD的道路小目标检测算法RFG_SSD。在SSD网络结构的主干部分和检测部分之间，通过引入改进的特征金字塔网络结构，融合浅层和深层感受野的特征信息，以获得小目标语义信息丰富的特征图。将深层特征提取网络ResNet 50作为改进网络的主干特征提取网络，提高整体网络的检测精度。为加快网络运算速度，基于检测层结构，利用全局平均池化层代替全连接层，减少网络参数量。实验结果表明，与SSD、VGG16+SFPN等算法相比，该算法能够有效提高小目标检测性能，且加快检测速度，其在BDD100K数据集上的平均精度和检测速度分别为98.05%和85.56 frame/s，小目标检测个数相较于SSD算法提高3倍多。

Research on Road Small Target Detection with Improved SSD Algorithm

In a road scene, the resolution of small targets is low, and the characteristics are not obvious. Traditional target detection algorithms find it difficult to identify category and location information, resulting in low detection accuracy, slow detection speed, and a high missed detection rate.This study proposes a small target detection algorithm (RFG_SSD) based on an improved SSD.By introducing the improved Feature Pyramid Network(FPN) structure and integrating the feature information of shallow and deep receptive fields, which obtain a feature image with rich semantic information of small targets, to improve the performance of small target detection.The deep feature extraction network ResNet50 is used as the backbone of the improved network to improve detection accuracy of the overall network.To further accelerate the network operation speed based on the detection layer structure, the Global Average Pooling(GAP) layer is used to replace the full connection layer to reduce the number of network parameters. The experimental results on the BDD100K dataset show that compared with the SSD and VGG16+SFPN algorithms, this algorithm can effectively improve the performance of small-target detection and increase the detection speed.Its average accuracy and detection speed are 98.05% and 85.56 frame/s, respectively.The number of small target detections is more than three times higher than that of the SSD algorithm.