基于改进YOLOv8s的恶劣天气车辆行人检测方法

Improved YOLOv8s method for vehicle and pedestrian detection in adverse weather

针对恶劣天气条件下摄像头捕获图像时存在图像模糊以及光照分布不均等问题,导致了场景对比度的下降,进而增加了在图像中区分检测目标与背景的难度。为了提高在恶劣天气环境下车辆和行人的检测能力,本文提出了一种改进的YOLOv8s算法。首先,本文在YOLOv8s算法的基础上,利用可扩张残差结构对主干网络中的C2F模块进行了优化,增强了模型对环境变化的适应能力。同时,在主干网络的SPPF模块前置层引入了高效多尺度注意力机制,该机制能够更有效地捕获图像中丰富多变的多尺度特征。其次,针对YOLOv8s算法的检测头部进行了重新设计,在保持模型准确性的前提下,降低了模型的复杂度。最后,引入Wise-IoU改进YOLOv8s算法的回归损失函数,提高了算法的收敛速度和检测精度。实验结果表明,改进的YOLOv8s算法在恶劣天气条件下对车辆和行人检测的平均精度均值达到91.41%,相比原始算法提升了2.56%,同时模型参数量减少了8%,计算量降低了4.9 GFLOPs。相比于其他主流的目标检测算法,改进后的YOLOv8s算法在保证了实时性能的同时,满足了恶劣天气条件下的车辆和行人检测需求。

Addressing the issues of image blurring and uneven light distribution encountered when capturing images in adverse weather conditions,which lead to decreased scene contrast and subsequently increase the difficulty of distinguishing detection targets from the background in images,this paper proposes an improved YOLOv8s algorithm to enhance the detection capability of vehicles and pedestrians in harsh weather environments.Firstly,based on the YOLOv8s algorithm,this paper optimizes the C2F module in the backbone network with an expandable residual structure,enhancing the model′s adaptability to environmental changes.At the same time,an efficient multi-scale attention mechanism is introduced before the SPPF module in the backbone network,which can more effectively capture the rich and varied multi-scale features in images.Secondly,the detection head of the YOLOv8s algorithm is redesigned to reduce the model′s complexity while maintaining accuracy.Finally,the introduction of Wise-IoU improves the regression loss function of the YOLOv8s algorithm,enhancing the algorithm′s convergence speed and detection accuracy.Experimental results show that the improved YOLOv8s algorithm achieves an mean average precision of 91.41%on datasets for vehicle and pedestrian detection under adverse weather conditions,which is a 2.56%improvement over the original algorithm,with a model parameter reduction of 8%and a computational reduction of 4.9 GFLOPs.Compared to other mainstream object detection algorithms,the significantly improved YOLOv8s algorithm not only ensures real-time performance but also effectively meets the challenging requirements for vehicle and pedestrian detection under adverse weather conditions.