【摘要】 针对校园智能清扫车障碍物检测精确度低、检测速度慢以及模型复杂度高的问题,提出一种改进YOLOv8s的校园智能清扫车障碍物检测与测距算法YOLOv8s-FDR。在YOLOv8s算法框架的基础上,将主干网络替换为参数量和内存访问量更小的Faster Net网络,以降低模型复杂度并提高检测速度;然后设计了SPPF-DAM模块,以残差方式引入可变形注意力机制,提高模型对多尺度目标特征的感知能力;其次,在特征融合网络中采用Partial-RFEM进行下采样,以捕获非局部上下文特征和局部目标特征,提高检测精确度;最后,添加了测距功能,降低硬件成本。实验结果表明,改进算法与原算法相比m AP提高了3.6%,模型计算量和参数量相较于原模型分别降低了19.72%和15.27%。实际环境测试显示,YOLOv8s-FDR算法的检测速度达到38.44 fps,远高于原算法的17.12 fps,能够满足校园智能清扫车正常运行的性能要求。更多还原

【Abstract】 In order to solve the problems of low accuracy, slow detection speed and high model complexity of obstacle detection of campus intelligent sweepers, a modified YOLOv8s obstacle detection and ranging algorithm for campus intelligent sweepers YOLOv8s-FDR was proposed. On the basis of the YOLOv8s algorithm framework, the backbone network is replaced by the Faster Net network with smaller parameters and memory access, so as to reduce the complexity of the model and improve the detection speed. Then, the SPPF-DAM module was designed to introduce the deformable attention mechanism(DAM) in the form of residuals to improve the model’s perception of multi-scale target features. Secondly, Partial-RFEM was used for downsampling in the feature fusion network to capture the nonlocal context features and local target features to improve the detection accuracy. Finally, the ranging function is added to reduce hardware costs. Experimental results show that compared with the original algorithm, the m AP of the improved algorithm is increased by 3.6%, and the amount of model computation and parameters is reduced by 19.72%and 15.27%, respectively. The actual environment test shows that the detection speed of the YOLOv8s-FDR algorithm reaches 38.44 fps, which is much higher than the 17.12 fps of the original algorithm, which can meet the performance requirements of the normal operation of the campus intelligent sweeper.更多还原