现有的高精度目标检测算法依赖于超深的主干网络(如ResNet和Inception),无法满足实时目标检测场景的需要,相反采用轻量级主干网络(如VGG-16和MobileNet)能达到实时目标检测的目的,但会导致检测精度的损失,对小目标的检测效果变差。SSD(Single Shot Multi-Box Detector)算法具有高精度、实时检测的特点。本文以SSD算法的网络结构为基础,通过添加感受野模块增强轻量级主干网络的特征提取能力,同时引入特征融合模块,充分利用深层网络提取语义信息,达到实时目标检测的目的,同时提高算法整体的检测精度和对小目标的检测能力。为进一步验证引入新模块的有效性,本文算法模型在PASCAL VOC2007数据集上进行测试,准确率达到80.5%,相比于原始SSD算法有3.3个百分点的提升,检测速度达到75 frame/s,整体性能优于目前大多数目标检测算法。

Existing high precision object detection algorithms mostly rely on super deep backbone networks, such as ResNet and Inception, making it difficult to meet real-time detection requirements. On the contrary, some lightweight backbone networks, such as VGG-16 and MobileNet, fulfill real-time processing but their accuracies are often criticized, especially when the targets are small. In this study, we explore an alternative to build a fast and accurate detector by strengthening the feature extraction ability of lightweight backbone networks, using a new receptive field block based on a single shot multi-box detector (SSD). Simultaneously, to make full use of the semantic information extracted from deep networks, a feature fusion module is designed and added, thereby improving the overall accuracy and enhancing the detection effect of the model for small targets, while still achieving real-time detection. To further verify the validity of introducing new modules, we have tested our model on the PASCAL VOC2007 data set and achieved an accuracy of 80.5% which is 3.3 percentage points higher than that of the original SSD model. In addition, the detection speed of the proposed model reaches 75 frame/s, and its overall performance is better than that of most of the current models.