A Real-Time Safety Detector Based on Re-parameterization Multiscale Feature Fusion for Forklift Driving

The application of object detection in intelligent logistics has received considerable attention. However, existing detector models face challenges such as high computational costs, slow detection speed, and difficulty in deployment on edge devices with limited computational resources. This paper proposes a novel real-time safety detector model (RMFFDet) based on YOLOv8s for forklift driving. A hardware-friendly FasterNeXt module is designed to optimize feature extraction and reduce the computational costs in the Backbone. Inspired by the work on RepGhost, a re-parameterization multiscale feature fusion Neck (RMFFNeck) is proposed in this paper. Reconstructing the Neck based on RMFFNeck improves the capture of contextual logistics background feature information while reducing the model parameters. Finally, the Wise-IoU (WIoU) is introduced as a bounding box regression loss combined with a dynamic non-monotonic focusing mechanism to improve the model’s overall performance. Experiments show that RMFFDet achieves a mean Average Precision (mAP) of 95.2% on the KITTI dataset and 92.8% on the self-built Forklift-3k dataset. Compared to YOLOv8s, the model parameters are reduced by 34.5%. On the Jetson Nano edge platform and 640 \(\times \) 640 input size, RMFFDet requires only 100.2ms inference time. RMFFDet offers an excellent trade-off between inference speed and detection accuracy. It meets the industrial requirements of logistics scenarios.