# Enhanced Small Target Detection via Multi-Modal Fusion and Attention Mechanisms: A YOLOv5 Approach

arXiv: https://arxiv.org/abs/2504.11262 · HTML: https://mlsanity.com/paper/2504.11262/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 16 references that are in the map.
- First posted on arXiv 15 Apr 2025.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021)
- [Accelerating the Super-Resolution Convolutional Neural Network](https://mlsanity.com/paper/1608.00367/) (2016)
- [Libra R-CNN: Towards Balanced Learning for Object Detection](https://mlsanity.com/paper/1904.02701/) (2019)
- [Context R-CNN: Long Term Temporal Context for Per-Camera Object Detection](https://mlsanity.com/paper/1912.03538/) (2019)
- [Anti-UAV: A Large Multi-Modal Benchmark for UAV Tracking](https://mlsanity.com/paper/2101.08466/) (2021)
- [HRDNet: High-resolution Detection Network for Small Objects](https://mlsanity.com/paper/2006.07607/) (2020)

## Abstract (arXiv)

With the rapid development of information technology, modern warfare increasingly relies on intelligence, making small target detection critical in military applications. The growing demand for efficient, real-time detection has created challenges in identifying small targets in complex environments due to interference. To address this, we propose a small target detection method based on multi-modal image fusion and attention mechanisms. This method leverages YOLOv5, integrating infrared and visible light data along with a convolutional attention module to enhance detection performance. The process begins with multi-modal dataset registration using feature point matching, ensuring accurate network training. By combining infrared and visible light features with attention mechanisms, the model improves detection accuracy and robustness. Experimental results on anti-UAV and Visdrone datasets demonstrate the effectiveness and practicality of our approach, achieving superior detection results for small and dim targets.