针对现有交通灯算法对小目标、遮挡目标检测识别效果不佳等问题,提出一种基于注意力与多尺度特征融合的YOLOv5检测算法(YOLOv5 detection algorithm based on attention and multi-scale feature fusion,AM-YOLOv5)。通过在残差结构中...针对现有交通灯算法对小目标、遮挡目标检测识别效果不佳等问题,提出一种基于注意力与多尺度特征融合的YOLOv5检测算法(YOLOv5 detection algorithm based on attention and multi-scale feature fusion,AM-YOLOv5)。通过在残差结构中引入坐标注意力模块,提高对小目标的特征提取能力;设计四尺度检测层,通过引入更浅层特征改善对小尺度目标的检测性能,提高检测精度;针对引入注意力和检测层导致计算量增大、速度降低的问题,采用分布移位卷积替换部分主干卷积的方法,简化模型,提升速度。实验结果表明:该算法在Lara数据集上平均精度均值达到90.8%,相较于经典YOLOv5算法,精度提升2.7%,速度达到59.9 FPS,在复杂恶劣环境下的BDD100K数据集上,精度提升3.6%,速度达到34.8 FPS,具有良好的检测效果,能较好地满足交通灯的实时检测。展开更多
Detecting small objects is a challenging task.We focus on a special case:the detection and classification of traffic signals in street views.We present a novel framework that utilizes a visual attention model to make ...Detecting small objects is a challenging task.We focus on a special case:the detection and classification of traffic signals in street views.We present a novel framework that utilizes a visual attention model to make detection more efficient,without loss of accuracy,and which generalizes.The attention model is designed to generate a small set of candidate regions at a suitable scale so that small targets can be better located and classified.In order to evaluate our method in the context of traffic signal detection,we have built a traffic light benchmark with over 15,000 traffic light instances,based on Tencent street view panoramas.We have tested our method both on the dataset we have built and the Tsinghua–Tencent 100K(TT100K)traffic sign benchmark.Experiments show that our method has superior detection performance and is quicker than the general faster RCNN object detection framework on both datasets.It is competitive with state-of-theart specialist traffic sign detectors on TT100K,but is an order of magnitude faster.To show generality,we tested it on the LISA dataset without tuning,and obtained an average precision in excess of 90%.展开更多
文摘针对现有交通灯算法对小目标、遮挡目标检测识别效果不佳等问题,提出一种基于注意力与多尺度特征融合的YOLOv5检测算法(YOLOv5 detection algorithm based on attention and multi-scale feature fusion,AM-YOLOv5)。通过在残差结构中引入坐标注意力模块,提高对小目标的特征提取能力;设计四尺度检测层,通过引入更浅层特征改善对小尺度目标的检测性能,提高检测精度;针对引入注意力和检测层导致计算量增大、速度降低的问题,采用分布移位卷积替换部分主干卷积的方法,简化模型,提升速度。实验结果表明:该算法在Lara数据集上平均精度均值达到90.8%,相较于经典YOLOv5算法,精度提升2.7%,速度达到59.9 FPS,在复杂恶劣环境下的BDD100K数据集上,精度提升3.6%,速度达到34.8 FPS,具有良好的检测效果,能较好地满足交通灯的实时检测。
基金supported by the National Natural Science Foundation of China (No.61772298)Research Grant of Beijing Higher Institution Engineering Research Centerthe Tsinghua–Tencent Joint Laboratory for Internet Innovation Technology
文摘Detecting small objects is a challenging task.We focus on a special case:the detection and classification of traffic signals in street views.We present a novel framework that utilizes a visual attention model to make detection more efficient,without loss of accuracy,and which generalizes.The attention model is designed to generate a small set of candidate regions at a suitable scale so that small targets can be better located and classified.In order to evaluate our method in the context of traffic signal detection,we have built a traffic light benchmark with over 15,000 traffic light instances,based on Tencent street view panoramas.We have tested our method both on the dataset we have built and the Tsinghua–Tencent 100K(TT100K)traffic sign benchmark.Experiments show that our method has superior detection performance and is quicker than the general faster RCNN object detection framework on both datasets.It is competitive with state-of-theart specialist traffic sign detectors on TT100K,but is an order of magnitude faster.To show generality,we tested it on the LISA dataset without tuning,and obtained an average precision in excess of 90%.
