Improving Generalization Performance of YOLOv8 for Camera Trap Object Detection

TL;DR

This paper enhances YOLOv8 for camera trap wildlife detection by integrating attention mechanisms and improved loss functions, significantly boosting its ability to generalize across diverse real-world environments.

Contribution

The study introduces specific modifications to YOLOv8, including a Global Attention Mechanism, multi-scale feature fusion, and a new bounding box loss, to improve its generalization in wildlife camera trap images.

Findings

Enhanced model suppresses background noise effectively

Improved focus on object features in diverse environments

Demonstrated robust generalization in unseen datasets

Abstract

Camera traps have become integral tools in wildlife conservation, providing non-intrusive means to monitor and study wildlife in their natural habitats. The utilization of object detection algorithms to automate species identification from Camera Trap images is of huge importance for research and conservation purposes. However, the generalization issue, where the trained model is unable to apply its learnings to a never-before-seen dataset, is prevalent. This thesis explores the enhancements made to the YOLOv8 object detection algorithm to address the problem of generalization. The study delves into the limitations of the baseline YOLOv8 model, emphasizing its struggles with generalization in real-world environments. To overcome these limitations, enhancements are proposed, including the incorporation of a Global Attention Mechanism (GAM) module, modified multi-scale feature fusion, and Wise Intersection over Union (WIoUv3) as a bounding box regression loss function. A thorough evaluation and ablation experiments reveal the improved model's ability to suppress the background noise, focus on object properties, and exhibit robust generalization in novel environments. The proposed enhancements not only address the challenges inherent in camera trap datasets but also pave the way for broader applicability in real-world conservation scenarios, ultimately aiding in the effective management of wildlife populations and habitats.