A lightweight model FDM-YOLO for small target improvement based on YOLOv8

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 attention mechanism.The FDM-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.

SAFE: Slow and Fast Parameter-Efficient Tuning for Continual Learning with Pre-Trained Models

Continual learning aims to incrementally acquire new concepts in data streams while resisting forgetting previous knowledge. With the rise of powerful pre-trained models (PTMs), there is a growing interest in training incremental learning systems using these foundation models, rather than learning from scratch. Existing works often view PTMs as a strong initial point and directly apply parameter-efficient tuning (PET) in the first session for adapting to downstream tasks. In the following sessions, most methods freeze model parameters for tackling forgetting issues. However, applying PET directly to downstream data cannot fully explore the inherent knowledge in PTMs. Additionally, freezing the parameters in incremental sessions hinders models' plasticity to novel concepts not covered in the first session. To solve the above issues, we propose a Slow And Fast parameter-Efficient tuning (SAFE) framework. In particular, to inherit general knowledge from foundation models, we include a transfer loss function by measuring the correlation between the PTM and the PET-applied model. After calibrating in the first session, the slow efficient tuning parameters can capture more informative features, improving generalization to incoming classes. Moreover, to further incorporate novel concepts, we strike a balance between stability and plasticity by fixing slow efficient tuning parameters and continuously updating the fast ones. Specifically, a cross-classification loss with feature alignment is proposed to circumvent catastrophic forgetting. During inference, we introduce an entropy-based aggregation strategy to dynamically utilize the complementarity in the slow and fast learners. Extensive experiments on seven benchmark datasets verify the effectiveness of our method by significantly surpassing the state-of-the-art.