Abstract
This paper introduces a Deep Learning Convolutional Neural Network model based on Faster-RCNN for motorcycle detection and classification on urban environments. The model is evaluated in occluded scenarios where more than 60% of the vehicles present a degree of occlusion. For training and evaluation, we introduce a new dataset of 7500 annotated images, captured under real traffic scenes, using a drone mounted camera. Several tests were carried out to design the network, achieving promising results of 75% in average precision (AP), even with the high number of occluded motorbikes, the low angle of capture and the moving camera. The model is also evaluated on low occlusions datasets, reaching results of up to 92% in AP.
Abstract (translated)
本文介绍了一种基于Faster-RCNN的深度学习卷积神经网络模型,用于摩托车在城市环境中的检测和分类。该模型在封闭场景中进行评估,其中超过60%的车辆存在一定程度的闭塞。对于培训和评估,我们使用无人机安装的摄像机引入了一个新的数据集,其中包含7500个带注释的图像,这些图像是在真实交通场景下捕获的。进行了几项测试以设计网络,即使在大量闭塞的摩托车,低角度捕获和移动摄像机的情况下,也能实现75%的平均精度(AP)的有希望的结果。该模型还在低遮挡数据集上进行评估,在AP中达到高达92%的结果。
URL
https://arxiv.org/abs/1808.02299