基于改进Faster RCNN的城市道路货车检测

针对货车利用躲避摄像头等手段在城市道路中不按规定时间、规定线路行驶, 使得车辆不能被准确识别的问题, 提出基于改进Faster RCNN的城市道路货车检测方法. 该方法以Faster RCNN为基础模型, 通过对传入主干网络的车辆图片进行卷积和池化等操作来提取特征, 其中增加特征金字塔网络(FPN)提升对多尺度目标检测的精度; 同时将K-means聚类算法应用在数据集上以获取新的锚点框; 利用RPN (region proposal network)生成建议框; 并使用CIoU (complete-IoU)损失函数代替原算法的smoothL1损失函数以提升检测车辆的精确性. 实验结果显示, 改进后的Faster RCNN相比原算法对货车检测的平均精度(AP)提高7.2%, 召回率(recall)提高6.1%, 减少了漏检的可能, 在不同场景下具有良好的检测效果.

Detection of Urban Trucks Based on Improved Faster RCNN

Trucks cannot be accurately identified when they do not follow the prescribed time and route on urban roads by avoiding cameras and other means. In view of this, an urban road truck detection method based on improved Faster RCNN is proposed. Features are extracted by performing convolution and pooling operations on the vehicle images passed into the backbone network. The feature pyramid network (FPN) is added to improve the accuracy of multi-scale target detection. At the same time, the K-means clustering algorithm is applied to the dataset to obtain new anchor boxes. Region proposal network (RPN) is utilized to generate proposal boxes and complete-IoU (CIoU) loss function is used for replacing the smoothL1 loss function of the original algorithm to improve the accuracy of vehicle detection. The experimental results show that the improved Faster RCNN increases the average precision (AP) for truck detection by 7.2% and the recall by 6.1%. The improved method reduces the possibility of missed detection and has a good detection effect in different scenarios.