# SLR-Net: Lightweight and Accurate Detection of Weak Small Objects in Satellite Laser Ranging Imagery

## TL;DR

This paper introduces a lightweight model for accurately detecting small objects in satellite laser ranging images, improving efficiency and precision.

## Contribution

The novel DMS-Conv module, LUM, and MPD-IoU Loss enhance feature extraction and localization for small targets without high computational cost.

## Key findings

- The model achieves an mAP50:95 of 47.13% and an F1-score of 88.24% on a real-world SLR dataset.
- The model uses only 2.57 M parameters and 6.7 GFLOPs, making it lightweight and efficient.
- The proposed methods outperform mainstream lightweight detectors in precision and recall for small target detection.

## Abstract

To address the challenges of insufficient efficiency and accuracy in traditional detection models caused by minute target sizes, low signal-to-noise ratios (SNRs), and feature volatility in Satellite Laser Ranging (SLR) images, this paper proposes an efficient, lightweight, and high-precision detection model. The core motivation of this study is to fundamentally enhance the model’s capabilities in feature extraction, fusion, and localization for minute and blurred targets through a specifically designed network architecture and loss function, without significantly increasing the computational burden. To achieve this goal, we first design a DMS-Conv module. By employing dense sampling and channel function separation strategies, this module effectively expands the receptive field while avoiding the high computational overhead and sampling artifacts associated with traditional multi-scale methods, thereby significantly improving feature representation for faint targets. Secondly, to optimize information flow within the feature pyramid, we propose a Lightweight Upsampling Module (LUM). Integrating depthwise separable convolutions with a channel reshuffling mechanism, this module replaces traditional transposed convolutions at a minimal computational cost, facilitating more efficient multi-scale feature fusion. Finally, addressing the stringent requirements for small target localization accuracy, we introduce the MPD-IoU Loss. By incorporating the diagonal distance of bounding boxes as a geometric penalty term, this loss function provides finer and more direct spatial alignment constraints for model training, effectively boosting localization precision. Experimental results on a self-constructed real-world SLR observation dataset demonstrate that the proposed model achieves an mAP50:95 of 47.13% and an F1-score of 88.24%, with only 2.57 M parameters and 6.7 GFLOPs. Outperforming various mainstream lightweight detectors in the comprehensive performance of precision and recall, these results validate that our method effectively resolves the small target detection challenges in SLR scenarios while maintaining a lightweight design, exhibiting superior performance and practical value.