| 摘 要: 为了克服目标检测算法在交通图像识别领域对数据集利用不充分、对小物体检测不敏感等问题，提出了一种基于SSD算法改进的检测模型。选择自动驾驶领域最重要的测试集作为模型训练的数据集，通过对比实验，选择出训练集、验证集和测试集最合适的划分比例。实验结果显示，合理的数据集划分相较于其他的对照组对于检测目标的准确率提升了13%，检测时间缩短了15%，证明合理的数据集划分能够提升模型泛化能力和检测效率。针对该算法对于小物体检测不敏感这一问题，有针对性地调整了模型的结构及参数，并修改了模型输入图像的尺寸。实验结果表明，在输入相同图片尺寸下，模型对于小物体的检测能力显著提升，整体检测能力提升了14.5%，且保证了较高的检测速率。以上均证明新算法的有效性。 |
| 关键词: 深度学习；计算机视觉；目标检测；SSD |
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中图分类号: TP311.5
文献标识码: A
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| 基金项目: 2018年度辽宁省自然科学基金项目《基于数据融合和V2X减速预警模型的研究》. |
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| Research and Application of Traffic Image Recognition based on Deep Learning |
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ZHANG Kangming, CAO Xin
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( Dalian Neusoft University of Information, Dalian 116023, China)
zhangkangming16@dnui.edu.cn; caoxin@neusoft.edu.cn
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| Abstract: Aiming at problems of insufficient utilization of datasets and insensitivity to small object detection in the field of traffic image recognition by target detection algorithm, this paper proposes an improved detection model based on SSD (Solid State Disk) algorithm. The most important test set in the field of autonomous driving is selected as the dataset for model training. Through comparative experiments, the most appropriate division ratio of training set, validation set and test set is selected. Experimental results show that compared with other control groups, reasonable dataset division has an increase of 13% in accuracy of detecting targets and a decrease of 15% in detection time, which proves that reasonable dataset division can improve model generalization and detection efficiency. Aiming at the problem that the algorithm is not sensitive to small object detection, structure and parameters of the model is adjusted and size of the input image of the model is modified. The experimental results show that under the same image input size, the detection capability of small objects is significantly improved, the overall detection capability is improved by 14.5%, and a higher detection rate is guaranteed. The above all prove the effectiveness of the new algorithm. |
| Keywords: deep learning; computer vision; target detection; SSD |
