The invention discloses a multi-scale vehicle target detection method suitable for a highway monitoring scene, and belongs to the field of intelligent traffic. According to the vehicle target detection method, a YOLOv8 backbone network is improved on the basis of a ConvNeXt V2 network, and an SPD-Conv module and an SA-Conv module are introduced into the ConvNeXt V2 network, so that the multi-scale feature expression capability of a model is enhanced; secondly, a feature pyramid network HS-FPN is adopted to carry out fusion enhancement on a feature map extracted by the backbone network so as to cope with multi-scale detection of a vehicle target; and finally, carrying out classification and regression on the enhanced multi-scale feature map by adopting a detection head with a classification task and a regression task being mutually decoupled, and finally obtaining a detection result of the vehicle target. According to the method, the accuracy of multi-scale vehicle target feature extraction of the highway monitoring scene can be effectively improved, the conditions of large target scale change, many interference factors and the like in the scene can be better handled, and the detection accuracy can be improved while the detection efficiency is ensured.
本发明公开了一种适于高速公路监控场景的多尺度车辆目标检测方法,属于智能交通领域。本发明车辆目标检测方法,以ConvNeXt V2网络为基础对YOLOv8骨干网络进行改进,并向ConvNeXt V2网络中引入SPD‑Conv模块和SA‑Conv模块,用于强化模型的多尺度特征表达能力;其次,采用特征金字塔网络HS‑FPN对骨干网络提取到的特征图进行融合增强,以应对车辆目标的多尺度检测;最后,采用分类任务和回归任务相互解耦的检测头对增强的多尺度特征图进行分类与回归,最终得到车辆目标的检测结果。本发明能够有效提高高速公路监控场景的多尺度车辆目标特征提取的精确度,更好地应对场景中目标尺度变化大、干扰因素多等状况,可在保证检测效率的同时提升检测精度。
Multi-scale vehicle target detection method suitable for highway monitoring scene
适于高速公路监控场景的多尺度车辆目标检测方法
16.07.2024
Patent
Elektronische Ressource
Chinesisch
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