Summary of Improving Generalization Performance Of Yolov8 For Camera Trap Object Detection, by Aroj Subedi
Improving Generalization Performance of YOLOv8 for Camera Trap Object Detection
by Aroj Subedi
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The thesis explores enhancements to the YOLOv8 object detection algorithm to improve generalization in Camera Trap images. The baseline model struggles with generalization in real-world environments due to limitations such as background noise and object properties. To address this, the study proposes the incorporation of a Global Attention Mechanism (GAM) module, modified multi-scale feature fusion, and Wise Intersection over Union (WIoUv3) bounding box regression loss function. Evaluation and ablation experiments reveal the improved model’s ability to suppress background noise, focus on object properties, and exhibit robust generalization in novel environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Camera traps are an important tool for wildlife conservation. Researchers use them to study animals without disturbing their natural behavior. However, identifying animal species from camera trap images is a challenging task that requires advanced computer algorithms. This thesis develops new techniques to improve the accuracy of these algorithms. The goal is to create a model that can identify animal species in different environments and situations. To achieve this, the researchers make improvements to the YOLOv8 algorithm by adding new features such as attention mechanisms and modified fusion processes. They test their approach using real-world camera trap data and show that it can effectively identify animal species even when they are not familiar with the environment. |
Keywords
» Artificial intelligence » Attention » Bounding box » Generalization » Loss function » Object detection » Regression
