Bird Object Detection: Dataset Construction, Model Performance Evaluation, and Model Lightweighting

Abstract

The application of object detection technology has a positive auxiliary role in advancing the intelligence of bird recognition and enhancing the convenience of bird field surveys. However, challenges arise due to the absence of dedicated bird datasets and evaluation benchmarks. To address this, we have not only constructed the largest known bird object detection dataset, but also compared the performances of eight mainstream detection models on bird object detection tasks and proposed feasible approaches for model lightweighting in bird object detection. Our constructed bird detection dataset of GBDD1433-2023, includes 1433 globally common bird species and 148,000 manually annotated bird images. Based on this dataset, two-stage detection models like Faster R-CNN and Cascade R-CNN demonstrated superior performances, achieving a Mean Average Precision (mAP) of 73.7% compared to one-stage models. In addition, compared to one-stage object detection models, two-stage object detection models have a stronger robustness to variations in foreground image scaling and background interference in bird images. On bird counting tasks, the accuracy ranged between 60.8% to 77.2% for up to five birds in an image, but this decreased sharply beyond that count, suggesting limitations of object detection models in multi-bird counting tasks. Finally, we proposed an adaptive localization distillation method for one-stage lightweight object detection models that are suitable for offline deployment, which improved the performance of the relevant models. Overall, our work furnishes an enriched dataset and practice guidelines for selecting suitable bird detection models.

MCFP-YOLO Animal Species Detector for Embedded Systems

“…Deploying deep learning-based animal detection models on embedded devices and reducing the processing delay, which is crucial for real-time applications, poses challenges due to their power constraints. Wang et al [2] offer insights into the development of lightweight models for avian species. This study addresses the balance between model performance and computational efficiency, a key consideration in power-constrained environments.…”

“…However, in reality, it is impossible that ∆i (x, y) will be equal to zero due to the presence of noise. In order to decide if these obtained non-zero values are caused by noise or motion, ∆i (x, y) is compared with a predefined threshold (Th), as shown in Equation (2).…”