A novel target-oriented enhanced infrared camera trap data screening method

Infrared Camera Traps (ICTs) are widely used in ecological research as a noninvasive wildlife monitoring technique, particularly for the detection and identification of animal targets. Existing ICT data screening methods face challenges in recognizing animals against complex backgrounds, particularly fast-moving or small targets. To address these issues, we proposed a target-oriented enhanced data-screening method called GFD-YOLO, which emphasized key locations in images to effectively guide the focus of the model toward target regions, thereby improving detection accuracy. We compared the effects of different preprocessing methods on detection performance. Results revealed that the proposed method improved the mean Average Precision (mAP) by 16.96%, precision by 10.13%, and recall by 24.85% compared to the YOLOv11n model. Therefore, the preprocessing method proposed in this study had significant advantages in reducing false negatives and false positives and was adaptable to wildlife detection tasks under different background conditions. In addition, this method demonstrated higher robustness in scenarios involving lighting variations and fast-moving targets.