Research on pedestrian detection based on lightweight YOLOv8

For the YOLOv8 pedestrian detection issue on embedded devices with high computational complexity and deployment challenges, we propose a novel lightweight pedestrian detection solution. This involves replacing YOLO's backbone network with lightweight models, MobileNetV3 and EfficientNetv2, and modifying the attention mechanism in the model by introducing the CBAM attention mechanism. Additionally, an SPPF module is added to the last layer of the model to enhance feature extraction. To achieve lightweighting in the head segment, we halved its parameter count. Experimental results show that compared to directly replacing the network, our approach successfully reduces the parameter count while achieving higher accuracy.