基于SqueezeNet和YOLOv2的交通违法证据评价

针对交通监控反向抓拍交通违法图像预判率高的问题,提出了一种基于迁移学习的多尺度交通违法证据评价方法.构建了以SqueezeNet为特征提取层、YOLOv2为目标检测层融合高分辨率细粒度特征的检测网络.通过卷积神经网络算法训练该模型学习抓拍车辆图像特征,识别图像中唯一交通违法车辆,再次训练识别驾驶员所在中心区域.在保证特...

Traffic Violation Evidence Evaluation Based on YOLOv2 and SqueezeNet

Aiming at the problem of the high predictive rate of traffic violation images captured by reverse traffic monitoring, a multi-scale traffic violation evidence evaluation method based on migration learning is proposed. A detection network is constructed that uses SqueezeNet as the feature extraction layer and YOLOv2 as the target detection layer to fuse high-resolution fine-grained features. Through the convolutional neural network algorithm, the model is trained to learn the characteristics of the captured vehicle image, identify the only illegal traffic vehicle in the image, and retrain to identify the central area where the driver is located. Under the condition that the ability of feature recognition and extraction remains unchanged, SqueezeNet is retrained by transfer learning to classify the image of the driver''s central area as good or bad, and the clear image of traffic violations is submitted to manual review. Experimental results show that this method improves the detection accuracy of illegal vehicles to 99.3%, the detection accuracy of key areas where the driver is located to 96.3%, and the image quality evaluation accuracy to 92.6%, which greatly reduces the workload of manual review.