Small targets are particularly difficult to detect due to their low pixel count, complex backgrounds, and varying shooting angles, which make it hard for models to extract effective features. While some large-scale models offer high accuracy, their long inference times make them unsuitable for real-time deployment on edge devices. On the other hand, models designed for low computational power often suffer from poor detection accuracy. This paper focuses on small target detection and explores methods for object detection under low computational constraints. Building on the YOLOv8 model, we propose a new network architecture called FDM-YOLO. Our research includes the following key contributions: We introduce FDM-YOLO by analyzing the output of the YOLOv8 detection head. We add a highresolution layer and remove the large target detection layer to better handle small targets. Based on PConv, we propose a lightweight network structure called Fast-C2f, which is integrated into the PAN module of the model. To mitigate the accuracy loss caused by model lightweighting, we employ dynamic upsampling (Dysample) and a lightweight EMA attentionthis http URLFDM-YOLO model was validated on the Visdrone dataset, achieving a 38% reduction in parameter count and improving the Map0.5 score from 38.4% to 42.5%, all while maintaining nearly the same inference speed. This demonstrates the effectiveness of our approach in balancing accuracy and efficiency for edge device deployment.
View on arXiv@article{zhang2025_2503.04452,
title={ A lightweight model FDM-YOLO for small target improvement based on YOLOv8 },
author={ Xuerui Zhang },
journal={arXiv preprint arXiv:2503.04452},
year={ 2025 }
}