# Multispectral Object Detection with Deep Learning

arXiv: https://arxiv.org/abs/2102.03115 · HTML: https://mlsanity.com/paper/2102.03115/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 50 references that are in the map.
- First posted on arXiv 5 Feb 2021.

## Builds on (most cited first, 9 of 9)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [Object Detection with Deep Learning: A Review](https://mlsanity.com/paper/1807.05511/) (2018)
- [Deep Learning for Generic Object Detection: A Survey](https://mlsanity.com/paper/1809.02165/) (2018)
- [RGB-T Object Tracking:Benchmark and Baseline](https://mlsanity.com/paper/1805.08982/) (2018)
- [Hierarchical LSTMs with Adaptive Attention for Visual Captioning](https://mlsanity.com/paper/1812.11004/) (2018)

## Built on by (most cited first, 3 of 3)

- [MLMT-CNN for Object Detection and Segmentation in Multi-layer and Multi-spectral Images](https://mlsanity.com/paper/2407.14473/) (2024)
- [Evaluating an Adaptive Multispectral Turret System for Autonomous Tracking Across Variable Illumination Conditions](https://mlsanity.com/paper/2512.22263/) (2025)
- [Surveying You Only Look Once (YOLO) Multispectral Object Detection Advancements, Applications And Challenges](https://mlsanity.com/paper/2409.12977/) (2024)

## Abstract (arXiv)

Object detection in natural scenes can be a challenging task. In many real-life situations, the visible spectrum is not suitable for traditional computer vision tasks. Moving outside the visible spectrum range, such as the thermal spectrum or the near-infrared (NIR) images, is much more beneficial in low visibility conditions, NIR images are very helpful for understanding the object's material quality. In this work, we have taken images with both the Thermal and NIR spectrum for the object detection task. As multi-spectral data with both Thermal and NIR is not available for the detection task, we needed to collect data ourselves. Data collection is a time-consuming process, and we faced many obstacles that we had to overcome. We train the YOLO v3 network from scratch to detect an object from multi-spectral images. Also, to avoid overfitting, we have done data augmentation and tune hyperparameters.