【摘要】 鸟类活动故障已经成为高速铁路的主要隐患之一,找出和清理接触网的鸟巢是一种应对手段。传统的鸟巢目标检测方法需要人工提取特征,而手工设计的特征难以在复杂的接触网场景中保证泛化能力。针对该问题,本文提出使用基于深度学习的目标检测算法识别接触网鸟巢,并提出一种基于一阶段目标检测模型RetinaNet的改进模型,增加P2特征层,扩充网络的感受野范围,以更好地检测出目标较小的鸟巢。最后使用高铁车载设备的数据集对基于深度学习的目标检测算法进行了训练和测试。实验结果表明:基于深度学习的目标检测算法在接触网鸟巢检测任务上表现优秀,且改进RetinaNet模型的mAP值达到了90.4%,优于原模型,对于高速铁路的避障任务具有参考和应用价值.更多还原

【Abstract】 At present,bird activity failure has become one of the main hidden dangers of high-speed railway. Finding and cleaning the birds’ nest of the catenary is a countermeasure. Traditional birds’ nest object detection methods require manual extraction of features,but hand-designed features are difficult to ensure generalization in complex contact network scenarios. To solve this problem,this paper proposes to use the deep learning based object detection algorithm to identify the birds’ nest on catenary. At the same time,an improved model based on the one-stage object detection model RetinaNet is proposed. The P2 feature layer is added to expand the receptive field range of the network,so that the smaller nest can be better detected. Finally,these deep learning based object detection algorithms are trained and tested using data sets collected by on-board equipment of high-speed railways. Experimental results show that the object detection algorithm based on deep learning is excellent in the catenary birds’ nest detection task,and the improved RetinaNet model has a mAP value of 90.4%,which is better than the original model. This algorithm has certain both reference and application value for the obstacle avoidance task of high-speed railway.更多还原