The invention relates to the field of automatic driving, and discloses an improved YOLOv8 model and an automatic driving road condition monitoring method based on the model, the model comprises a C2f-Faster-EMHA module adopting an EMHA attention mechanism, the C2f-Faster-EMHA module integrates a Faster Block structure and the EMHA attention mechanism, and the EMHA attention mechanism uses a Hardsigmoid activation function to replace a traditional Sigmoid activation function. The method comprises the following steps: S1, collecting image data in an automatic driving environment; s2, the image data is input into the improved YOLOv8 model, and the image data is input into the improved YOLOv8 model; and S3, outputting a detection result, wherein the detection result comprises but is not limited to positions and categories of vehicles, pedestrians and traffic signs. According to the automatic driving road condition monitoring method provided by the invention, the improved YOLOv8 model is combined, and a more efficient, more accurate and more reliable road condition monitoring capability is provided for an automatic driving system.
本申请涉及自动驾驶领域,公开了一种改进的YOLOv8模型以及基于该模型的自动驾驶路况监测方法,所述模型包括采用EMHA注意力机制的C2f‑Faster‑EMHA模块,所述C2f‑Faster‑EMHA模块集成了FasterBlock结构和EMHA注意力机制,其中EMHA注意力机制使用Hardsigmoid激活函数替换传统的Sigmoid激活函数。所述方法包括以下步骤:S1、采集自动驾驶环境下的图像数据;S2、将所述图像数据输入至所述改进后的YOLOv8模型;S3、输出检测结果,所述检测结果包括但不限于车辆、行人、交通标志的位置和类别。本发明提供的自动驾驶路况监测方法结合了改进后的YOLOv8模型,为自动驾驶系统提供了更高效、更准确和更可靠的路况监测能力。
Automatic driving road condition monitoring method based on improved YOLOv8
一种基于改进YOLOv8的自动驾驶路况监测方法
2024-04-26
Patent
Electronic Resource
Chinese
IPC: | G06V / B60W CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION , Gemeinsame Steuerung oder Regelung von Fahrzeug-Unteraggregaten verschiedenen Typs oder verschiedener Funktion / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen |
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