Currently, challenges such as small object size and occlusion lead to a lack of accuracy and robustness in small object detection. Since small objects occupy only a few pixels in an image, the extracted features are limited, and mainstream downsampling convolution operations further exacerbate feature loss. Additionally, due to the occlusion-prone nature of small objects and their higher sensitivity to localization deviations, conventional Intersection over Union (IoU) loss functions struggle to achieve stable convergence. To address these limitations, LR-Net is proposed for small object detection. Specifically, the proposed Lossless Feature Fusion (LFF) method transfers spatial features into the channel domain while leveraging a hybrid attention mechanism to focus on critical features, mitigating feature loss caused by downsampling. Furthermore, RSIoU is proposed to enhance the convergence performance of IoU-based losses for small objects. RSIoU corrects the inherent convergence direction issues in SIoU and proposes a penalty term as a Dynamic Focusing Mechanism parameter, enabling it to dynamically emphasize the loss contribution of small object samples. Ultimately, RSIoU significantly improves the convergence performance of the loss function for small objects, particularly under occlusion scenarios. Experiments demonstrate that LR-Net achieves significant improvements across various metrics on multiple datasets compared with YOLOv8n, achieving a 3.7% increase in mean Average Precision (AP) on the VisDrone2019 dataset, along with improvements of 3.3% on the AI-TOD dataset and 1.2% on the COCO dataset.

Although conventional object detection methods achieve high accuracy through extensively annotated datasets, acquiring such large-scale labeled data remains challenging and cost-prohibitive in numerous real-world applications. Few-shot object detection presents a new research idea that aims to localize and classify objects in images using only limited annotated examples. However, the inherent challenge in few-shot object detection lies in the insufficient sample diversity to fully characterize the sample feature distribution, which consequently impacts model performance. Inspired by contrastive learning principles, we propose an Implicit Feature Contrastive Learning (IFCL) module to address this limitation and augment feature diversity for more robust representational learning. This module generates augmented support sample features in a mixed feature space and implicitly contrasts them with query Region of Interest (RoI) features. This approach facilitates more comprehensive learning of both intra-class feature similarity and inter-class feature diversity, thereby enhancing the model’s object classification and localization capabilities. Extensive experiments on PASCAL VOC show that our method achieves a respective improvement of 3.2%, 1.8%, and 2.3% on 10-shot of three Novel Sets compared to the baseline model FPD.