Review on Detection of Objects in Camouflage Images

Abstract

Spotting camouflaged objects in tricky scenes is still one of the toughest nuts to crack in computer vision. These things are made to blend right in, whether by nature or design, so the differences between the object and its background are super subtle. In this study, we used a deep learning approach with YOLOv8 (You Only Look Once, version 8) to tackle camouflage detection head-on. The model pulls in cool features like C2f backbone blocks, SPPF pooling, PAN/FPN fusion, and attention mechanisms to really dig into the details and pinpoint locations. We also prepped the images with edge sharpening, frequency tweaks, and data boosts to bring out those hidden edges. Testing on top camouflage datasets like COD10K, CAMO, ACD1K, and NC4K showed our method outperforms the old-school stuff, hitting an average mAP of 88.5% and running in real-time at 30–35 FPS. Turns out, YOLOv8 does a bang-up job spotting these sneaky objects in all sorts of environments, which could help with military watch, wildlife tracking, disaster relief, and eco-studies.

Introduction

Camouflaged object detection (COD) is a challenging task in computer vision due to the low visual distinction between objects and their backgrounds. Traditional methods like edge detection or color segmentation struggle in such scenarios, while deep learning—especially YOLOv8—offers real-time, high-accuracy detection through anchor-free architecture, attention modules, and optimized loss functions. COD has critical applications in defense, wildlife monitoring, rescue missions, and environmental surveillance.

The study aims to develop a robust, efficient, and adaptive YOLOv8-based detection system capable of identifying camouflaged objects in complex backgrounds. Key components include:

• Dataset preparation using COD10K, CAMO, NC4K, and ACD1K with preprocessing (resizing, normalization, edge enhancement, augmentation).

• Architecture improvements with C2f+SPPF backbone, PAN/FPN neck with attention, and a YOLOv8 head for bounding box, class, and objectness prediction.

• Training with WIoU loss for precise localization and optimization for real-time performance.

• Evaluation metrics include mAP, precision, recall, IoU, and FPS.

Experimental results show high detection accuracy across datasets (mAP 85–90%, FPS 28–35) and strong reliability in identifying camouflaged objects, even in challenging visual conditions. Attention modules and multi-scale feature fusion significantly reduce false negatives and improve sensitivity to subtle textures and contrasts.

Conclusion

Our work shows YOLOv8 is a solid, speedy way to de- tect camouflaged objects in messy scenes. By mixing smart preprocessing, multi-scale fusion, and attention tweaks, we get top-notch accuracy and real-time vibes. The tests beat traditional methods, proving YOLOv8’s fit for camouflage. Looking ahead, we could slim it for devices, add infrared or thermal inputs, or build multi-modal systems for low-light or night ops. This pushes automated camouflage detection forward, helping defense, environment work, and smart vision tech.

References

[1] Bing Li, Enze Zhu, Rongqian Zhou, Huang Cheng, “MilInst: Enhanced Instance Segmentation Framework for Military Camouflaged Targets,” IEEE Access, Vol. 11, 2023. [2] Yantong Liu, Xiao Yang, Sai Che, Chen Xian, Liwei Ai, Chuanxiang Song, Zheyu Zhang, “YOLOv8-SIM: Camouflage Detection Optimiza- tion for Alligator sinensis,” Sensors, Vol. 24, 2024. [3] Prof. Swati Dronkar, Kunalsingh Bais, Ishan Jaiswal, Nikhil Khawase, Yash Dipke, Yash Channawar, “Camouflaged Object Detection System Using YOLOv8 Segmentation,” IRJET, Vol. 12, Issue 4, pp. 89–92, 2025.

Copyright

Copyright © 2025 Swejal R. Patade, Pallavi A. Chaudhari. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.