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Related Experiment Video

Updated: Jul 15, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Published on: December 15, 2023

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

Yang Wang^1,2, Jiaogen Zhou², Caiyun Zhang²

¹Department of Computer Science and Technology, Tongji University, Shanghai 201804, China.

Animals : an Open Access Journal From MDPI

|September 28, 2023

Summary

Researchers developed the largest bird object detection dataset (GBDD1433-2023) to improve bird recognition and field surveys. Two-stage models excelled, and new lightweight methods enhance offline deployment for bird identification.

Keywords:

adaptive localization distillation bird counting bird monitoring model lightweighting object detection

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Area of Science:

Computer Vision
Ornithology
Machine Learning

Background:

Object detection advances bird recognition and field surveys.
Lack of dedicated bird datasets and benchmarks hinders progress.

Purpose of the Study:

Construct the largest bird object detection dataset (GBDD1433-2023).
Evaluate mainstream object detection models for bird identification.
Propose lightweight models for bird detection.

Main Methods:

Created GBDD1433-2023 with 1433 species and 148,000 images.
Compared eight object detection models, including Faster R-CNN and Cascade R-CNN.
Developed an adaptive localization distillation for lightweight models.

Main Results:

Two-stage models achieved 73.7% mAP, outperforming one-stage models.
Two-stage models showed better robustness to scale and background variations.
Bird counting accuracy decreased significantly beyond five birds per image.

Conclusions:

GBDD1433-2023 dataset supports bird object detection research.
Two-stage models are superior for bird detection tasks.
Lightweight models with distillation are suitable for offline bird identification.