# Surveying You Only Look Once (YOLO) Multispectral Object Detection Advancements, Applications And Challenges

arXiv: https://arxiv.org/abs/2409.12977 · HTML: https://mlsanity.com/paper/2409.12977/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 35 of its 233 references that are in the map.
- First posted on arXiv 3 Sep 2024.

## Builds on (most cited first, 30 of 35)

- [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)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [YOLOv10: Real-Time End-to-End Object Detection](https://mlsanity.com/paper/2405.14458/) (2024)
- [YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information](https://mlsanity.com/paper/2402.13616/) (2024)
- [Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detection](https://mlsanity.com/paper/2203.16220/) (2022)
- [YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications](https://mlsanity.com/paper/2209.02976/) (2022)
- [A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS](https://mlsanity.com/paper/2304.00501/) (2023)
- [A Survey of Deep Meta-Learning](https://mlsanity.com/paper/2010.03522/) (2020)
- [PP-YOLOE: An evolved version of YOLO](https://mlsanity.com/paper/2203.16250/) (2022)
- [PP-YOLO: An Effective and Efficient Implementation of Object Detector](https://mlsanity.com/paper/2007.12099/) (2020)
- [DAMO-YOLO : A Report on Real-Time Object Detection Design](https://mlsanity.com/paper/2211.15444/) (2022)
- [PP-YOLOv2: A Practical Object Detector](https://mlsanity.com/paper/2104.10419/) (2021)
- [Object Detection Using Sim2Real Domain Randomization for Robotic Applications](https://mlsanity.com/paper/2208.04171/) (2022)
- [Snapshot Multispectral Imaging Using a Diffractive Optical Network](https://mlsanity.com/paper/2212.05217/) (2022)
- [YOLOv1 to YOLOv10: A comprehensive review of YOLO variants and their application in the agricultural domain](https://mlsanity.com/paper/2406.10139/) (2024)
- [Object Detection in Thermal Spectrum for Advanced Driver-Assistance Systems (ADAS)](https://mlsanity.com/paper/2109.09854/) (2021)
- [YOLOv5, YOLOv8 and YOLOv10: The Go-To Detectors for Real-time Vision](https://mlsanity.com/paper/2407.02988/) (2024)
- [Evaluation of Thermal Imaging on Embedded GPU Platforms for Application in Vehicular Assistance Systems](https://mlsanity.com/paper/2201.01661/) (2022)
- [YOLOv9 for Fracture Detection in Pediatric Wrist Trauma X-ray Images](https://mlsanity.com/paper/2403.11249/) (2024)
- [Enhanced Self-Checkout System for Retail Based on Improved YOLOv10](https://mlsanity.com/paper/2407.21308/) (2024)
- [YOLOv1 to YOLOv10: The fastest and most accurate real-time object detection systems](https://mlsanity.com/paper/2408.09332/) (2024)
- [Hy-Tracker: A Novel Framework for Enhancing Efficiency and Accuracy of Object Tracking in Hyperspectral Videos](https://mlsanity.com/paper/2311.18199/) (2023)
- [Automatic Cattle Identification using YOLOv5 and Mosaic Augmentation: A Comparative Analysis](https://mlsanity.com/paper/2210.11939/) (2022)
- [YOLOR-Based Multi-Task Learning](https://mlsanity.com/paper/2309.16921/) (2023)
- [Semantic segmentation of multispectral photoacoustic images using deep learning](https://mlsanity.com/paper/2105.09624/) (2021)

## Abstract (arXiv)

Multispectral imaging and deep learning have emerged as powerful tools supporting diverse use cases from autonomous vehicles, to agriculture, infrastructure monitoring and environmental assessment. The combination of these technologies has led to significant advancements in object detection, classification, and segmentation tasks in the non-visible light spectrum. This paper considers 400 total papers, reviewing 200 in detail to provide an authoritative meta-review of multispectral imaging technologies, deep learning models, and their applications, considering the evolution and adaptation of You Only Look Once (YOLO) methods. Ground-based collection is the most prevalent approach, totaling 63% of the papers reviewed, although uncrewed aerial systems (UAS) for YOLO-multispectral applications have doubled since 2020. The most prevalent sensor fusion is Red-Green-Blue (RGB) with Long-Wave Infrared (LWIR), comprising 39% of the literature. YOLOv5 remains the most used variant for adaption to multispectral applications, consisting of 33% of all modified YOLO models reviewed. 58% of multispectral-YOLO research is being conducted in China, with broadly similar research quality to other countries (with a mean journal impact factor of 4.45 versus 4.36 for papers not originating from Chinese institutions). Future research needs to focus on (i) developing adaptive YOLO architectures capable of handling diverse spectral inputs that do not require extensive architectural modifications, (ii) exploring methods to generate large synthetic multispectral datasets, (iii) advancing multispectral YOLO transfer learning techniques to address dataset scarcity, and (iv) innovating fusion research with other sensor types beyond RGB and LWIR.